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A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

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01 March 2024

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04 March 2024

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Abstract
The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.
Keywords: 
Subject: Computer Science and Mathematics  -   Signal Processing

1. Introduction

The Brain-Computer Interface (BCI) technology has emerged as a pioneering innovation with profound implications across various domains, particularly in the realm of healthcare. By bridging the gap between the human brain and external devices, BCI systems offer innovative solutions for diagnosis, treatment, and rehabilitation, thereby transforming the landscape of medical practice. BCI holds significant importance and has made substantial impacts across multiple fields, particularly in recent years. BCI technology has revolutionized numerous domains across various fields by facilitating direct communication between the human brain and external devices. Especially in healthcare, BCI has transformed diagnostics, treatment, and rehabilitation processes, offering personalized and targeted solutions for patients with neurological conditions and physical disabilities. Furthermore, numerous other applications are found in fields such as gaming, education, and human-computer interaction, enhancing user experiences and fostering innovation.
In this review paper, we present an extensive analysis of BCI applications, with a primary focus on its pivotal role in medical settings. Through this comprehensive analysis of relevant research papers, we aim to explore the diverse applications of BCI technology and its potential to revolutionize patient care. Moreover, BCI applications in medical practice are further segmented into distinct categories, facilitating a systematic exploration of its multifaceted impacts on the healthcare industry. The taxonomy adopted in this paper includes Accessibility, General Medical Applications, Psychology/Neurology, Pediatric Applications, and Personalized Medicine. The taxonomy is presented in Figure 1, including the number of papers reviewed in each section.

2. BCI System

The human brain is referred to as the most sophisticated organ, captivating the curiosity of researchers, scholars, and engineers for centuries. The capabilities and intricacies of the human brain is a fascinating and interesting source of exploration, pushing the boundaries of technology and neuroscience. One of the most notable achievements in this area is the development of the Brain-Computer Interface (BCI) system, which builds a remarkable connection between the human brain and computers or machines. BCI system represents an advanced merging of computer science, neuroscience and engineering while offering the potential scopes to interact with the human brain or mindset with technology. This technique empowers individuals with several disabilities, such as lost motor functionalities, paralysis etc.
At its core, BCI technology is sophisticatedly connected with capturing and interpreting psychological signals, such as Electrocardiography (ECG) and Electro cephalography (EEG). These signals serve as a gateway to unlock the remarkable potential of establishing a direct connection between the human mind and external devices. BCI leverages the electrical activities generated by the heart (ECG) or brain (EEG) while decoding emotions, thoughts, and intentions and translating the signals into meaningful instructions for machines, computers, and prosthetic devices. It is crucial to explore the relationship between the physiological signals and technology interfaces to understand the extraordinary capabilities of the advancement of BCI systems. The overall process of integrating the BCI system with EEG signals is illustrated in Figure 2.

Integration of BCI System with Physiological Signals EEG

The process of integrating Brain-Computer Interface (BCI) technology with psychological signals can be outlined as follows:
  • Signal Acquisition: This process begins with the signal acquisition from EEG or ECG experiment from the electrodes placed on the body (for ECG) or the scalp (for EEG). The electrical activities of brain and heart are represented by the signals [1,2].
  • Pre-processing: The extracted signals are then pre-processed to remove artifacts, noise, and any unwanted interferences [3]. This is a crucial step that ensures that the data is clean enough and suitable for further analysis.
  • Extracting Features: Relevant significant features are later extracted from these pre-processed signals, including amplitude value, components, frequency, or other key characteristics that carry insightful information about the users’ condition or state.
  • Processing Signals: The extracted features are required to undergo further signal processing stages that may involve transformation, filtering, or other mathematical and statistical operations to enhance the information and prepare it for detailed analysis.
  • Recognizing Patterns: Machine learning, deep learning algorithms or other pattern recognition techniques are employed to analyze the extracted features. These algorithms undergo training to identify and recognize signatures or patterns within the data that correspond to physiological or mental states [4].
  • Making Decisions: The BCI system makes determinations based on the patterns related to the user’s intentions or states [5]. As an illustration, it can determine whether the user wants to manipulate a cursor on a particular screen or initiate a command.
  • Controlling Command Generation: The BCI system converts the decision into a control command for an external application or device. Such command generation encompasses a wide range of potential actions, such as controlling robotic arms, computer cursors, wheelchairs, or any other devices or software aligned with users intend to interact with.
  • External Device’s Output: The generated control commands are transmitted into the external devices or applications, subsequently carrying out the desired actions or responses to the user’s intent.
  • End: The BCI system continues looping through these stages, allowing real-time interaction between users and external devices/applications. The process operates as continuous and adaptive, with the BCI system constantly updating its understanding of the user’s intentions.

3. Applications of BCI

The scope and applications of BCI are versatile within and beyond medical sphere. In the medical realm, it has been providing hope and transformative remedies for individuals with profound disabilities, facilitating to regain both mobility and communication skills. BCI systems are playing promising roles in assisting patients in recovering from neurological diseases and ailments [6]. There are numerous research works where scholars have worked on developing such systems. To gain deeper insights and understanding into the breadth of this research, it can be categorized into distinct taxonomies or segments.
BCI medical applications are diverse and promising ranging from advancing mobility to enhancing accessibility of patients. Numerous transformative solutions can improve the lives of numerous patients facing many complicated heath issues [7]. This medical application domain can be further thoughtfully divided into distinct categories to gain better comprehension of the vast scope and significance. The categories include ‘Accessibility’, which encompasses prosthetic control, rehabilitation, and wheelchair mobility solutions; ‘General Medical Applications’, which extend to communication support and neurological condition management; and ‘Psychology/Neurology’, which delves into areas such as Alzheimer’s disease detection, depression assessment, emotion classification, epilepsy monitoring, and stress evaluation. Each of these segments plays a important role in harnessing the potential of BCI technology to enhance healthcare outcomes, from restoring mobility and communication abilities to advancing our understanding of complex neurological and psychological conditions [8].

3.1. Accessibility

The objective of such studies is to research brain-computer interfaces (BCIs) on patients suffering from amyotrophic lateral sclerosis (ALS), brain stroke, brain/spinal cord injury, cerebral palsy, muscular dystrophy, and so forth. BCI accessibility applications represent a revolutionary advancement while improving the lives of individuals with physical limitations and communication challenges [9]. One specific application in this regard is showcased by Manyakov, Chumerin [10] where noninvasive BCIs are used based on electroencephalograms (EEG) recorded on the subject’s scalp requiring no surgical procedure. The event related potentials (ERPs) that was the focus of this study was electrophysiological response to an internal or external stimulus using the P300 BCI response. The study was conducted using the prototype of a miniature EEG recording device that communicates wirelessly with a USB stick receiver. This accessibility domain can be further segmented into prosthetic control, rehabilitation, and wheelchair mobility solutions.

3.1.1. Prosthetic Control

There are various techniques for controlling a prosthetic hand including a shoulder harness, myo-electric control, and the WILMER elbow. A shoulder harness requires movement of the upper arm or shoulder while myoelectric control requires some nerves or muscle activity in the amputated extremity. The WILMER elbow uses elbow motion to control the hand. However, these systems are not useful for patients with total paralysis, but an EEG-based BCI provides a new control channel for individuals with severe motor impairments. This involves detecting motor actions from the EEG to control an externally powered prosthesis device during grasping. The BCI can be controlled by a binary output signal obtained through the classification of EEG patterns during hand movement imagination. Utilizing oscillatory EEG components as input signals for a BCI necessitates real-time analysis of EEG signals.
Guger, Harkam [11] combined recent BCI developments with modern prosthetic tools. The BCI experiment involved the use of EEG to control a prosthesis through binary output signals, obtained by classifying EEG patterns during imagination of left- and right-hand movements. The EEG setup consisted of a minimum of two electrodes, positioned close to primary hand areas (C3 and C4), to capture oscillatory EEG components as input signals for the BCI. In another study, Miranda, Casebeer [12] used Blackrock Microsystems NeuroPort data acquisition system for recording prosthetic data. It was also used in converting neural firing rates into a functional mapping. For the prosthetic limb commands in endpoint velocity space, mathematical models were integrated into BCI systems to restore and/or facilitate near natural neural and behavioral function to advance neural decoder capabilities through multi-scale, dynamic models for the brain’s plastic changes.
Furthermore, Katyal, Johannes [13] developed a method for collaborating BCI approach for the autonomous control of a prosthetic limb system, enabling amputees to achieve more natural, efficient, and intuitive control of their prosthetic limbs. The project proposes a BCI system that uses a combination of electroencephalography (EEG) and electromyography (EMG) signals to facilitate the control of a prosthetic limb. The system uses a deep neural network (DNN) to classify and interpret the EEG and EMG signals, which are then used to control the prosthetic limb in real-time. Similarly, Laiwalla, Lee [14] proposed a distributed wireless network of sub-mm cortical microstimulators (SCMs) for brain-computer interfaces (BCIs). This system aims to enhance the functionality and performance of BCIs by enabling precise and targeted neural stimulation, thereby improving the control and feedback provided to the user.
In another study, Chapin, Moxon [15] used a linear decoder to map the recorded neural activity to the desired movement of the robotic arm in real-time. The decoding algorithm was implemented in MATLAB software and incorporated a Kalman filter to estimate the state of the arm and correct for errors in the decoding process. The authors report high accuracy and low latency of the neural interface in controlling the robotic arm. The study suggests the potential of the proposed neural interface for the development of advanced prosthetics for individuals with motor disabilities. Again, Oppus, Prado [16] described the design and development of a 3D-printed prosthetic hand that incorporates sensors for BCI control and a voice recognition module for voice commands. The authors reported successful testing of the prosthetic hand on a single user. This demonstrates the potential of this technology to improve the life quality of patients with upper limb amputations.
In the research works [17,18,19], authors attempted to implement machine learning based predictive modeling for decoding ERPs and understand the intend of robotic arms. A combination of EEG and EMG is also presented here to demonstrate that the use of such a BCI could improve the performance of a user’s control over a robotic arm. The results of the study from Aly, Youssef [18] showed that the hybrid BCI system using both EEG and EMG signals achieved an average classification accuracy of 81.9% for the grasping and releasing task. The extracted features from both EEG and EMG signals were used to train a machine learning model based on a Gaussian mixture model (GMM) to decode the user’s intended movement. The system was implemented using the OpenViBE platform and the MATLAB software was used for offline data analysis.

3.1.2. Rehabilitation

BCIs have primarily been studied for the purpose of providing assistive technologies to individuals with severe motor disabilities caused by neurodegenerative diseases or strokes. The use of BCIs for enhancing motor and cognitive recovery within neurorehabilitation settings is a newly emerging field of research. While most rehabilitation tools require minimal motor control, BCIs allow patients with severe motor deficits to participate in therapeutic tasks. EEG-based paradigms include sensorimotor rhythms, slow cortical potentials, event-related potentials, and visually evoked potentials are commonly used in this case [20].
Several studies have been conducted to assess cognitive functions in paralyzed ALS patients and in patients with physical disabilities due to neurological diseases [21,22,23,24,25]. The evaluation of cognitive abilities in patients with severe motor disabilities is a challenge and a less explored area, but some attempts have been made using event-related potentials. Three EEG-based modalities (SCP, SMR, and P300) are promising solutions for EEG-BCI system realization. While many studies have demonstrated successful BCI operation, others have shown low performance rates in terms of both CA and ITR [26]. P300-BCI exhibits higher ITRs but is greatly affected by the severity of the disease, while SMR-based BCI systems are adaptive but have the disadvantage of being unreliable for some subjects. Nevertheless, game-oriented solutions seem to be a promising way to enhance user motivation[27]. Despite high performance rates in some studies, the majority of BCI systems and applications are mainly used in a research environment and have yet to be successfully utilized in patients’ homes for continuous and everyday use[28].

3.1.3. Wheelchair Mobility

BCI system aids in wheelchair mobility by allowing individual with extreme mobility impairment to control the wheelchair’s movements using their brain signal. Users can get greater independence and improved mobility through this technology. Independent movement becomes easier to them while navigating their environment with ease. Researchers are conducting extensive experiments and clinical trials in designing such systems. In the context of BCI-controlled hands-free wheelchair navigations [29,30], scholars worked on developing system to detect the user’s mental commands and translate them into wheelchair movements, allowing people with severe physical disabilities to operate the wheelchair easily. Permana, Wijaya [30] worked on three machine learning models, namely linear discriminant analysis (LDA), support vector machine (SVM), and K-nearest neighbors (KNN), were trained on the EEG data to classify the mental tasks. The performance of the models was evaluated using accuracy, sensitivity, and specificity measures. The results showed that SVM outperformed the other models with an accuracy of 96.9% in classifying the mental tasks. The results of the study showed that the proposed brain-computer interface (BCI) system using NeuroSky MindWave Mobile 2 can accurately classify mental tasks with high accuracy using SVM machine learning model.
There are other systems based on motor imagery task stimulation for individuals with severe motor impairment, such as amyotrophic lateral sclerosis (ALS). In one such study Eidel, Tröger [31] worked on a participant who had restricted motor function. Vibrotactile stimuli were applied on four body positions of this patient, and EEG data were recorded from 12 positions (Fz, FC1, FC2, C3, Cz, C4, P3, Pz, P4, O1, Oz, and O2) using a g.GAMMAcap. The EEG data were filtered between 0.1 and 30 Hz, and epochs from -100 to 800 ms around the stimulus onset were created, rejecting epochs as artifacts if they contained excessive values (± 75 μV threshold). Similar studies was conducted by Huang, Zhang [32] and Meng, Zhang [33] where a hybrid brain-computer interface (hBCI) has been developed that can control an integrated wheelchair and robotic arm system.
Edelman, Meng [34] developed a BCI enhanced framework which could achieve more than 500% efficiency in pursuing continuous task through a real-time control robotic arm. Moreover, Belkacem, Jamil [35] have highlighted the issues related to age-sensitive cognitive functions and how the decline in memory, learning new skills, and paying attention to multiple tasks can affect the quality of life of older people. Additionally, rotation-aligned domain adaptation method with Riemannian mean (RMRA) can effectively handle cross-session and cross-subject issues in BCI, achieving satisfactory results in offline unsupervised and online experiments on different motor imagery EEG datasets [36].

3.1.4. Virtual Reality Accessibility

Virtual Reality (VR) Accessibility represents a significant leap forward in leveraging BCI technology to enhance the virtual experiences of individual with physical disabilities [37]. BCIs contribute to accessibility in virtual reality, allowing users with physical disabilities to interact within virtual spaces through brain signals [38]. This groundbreaking application empowers individual by enabling interaction within virtual spaces through the interpretation of brain signals.
In this context, to assist people with visual impairments (PVI), researchers developed VRBubble, an innovative audio-driven virtual reality technique that offers information about surrounding avatars based on their social distances [39]. VRBubble has been evaluated by an audio baseline of 12 PVI through a conversation and navigation context. This advancement in VR accessibility not just promotes inclusiveness but also unlocks fresh opportunities for education, and therapeutic applications, enhancing the virtual experience for a wider range of users [40].

3.1.5. Augmented and Alternative Communication

Augmented and Alternative Communication (AAC) encompasses the conversion of neural signals into meaningful communication providing a lifeline for those who are non-verbal or face difficulties in traditional communication methods. Numerous scholars have dedicated their efforts to develop BCI enabled solutions in this context [41,42,43,44]. In one such study [45], authors attempted to assess how individuals affected by amyotrophic lateral sclerosis (ALS) acquired the skill of operating a motor-based Brain-Computer Interface (BCI) switch within the context of a row-column Augmented and Alternative Communication scanning pattern. Additionally, the study aimed to explore person-centered factors linked to the performance of BCI-AAC. Such advancement of BCI in Augmented and Alternative Communication offers significant potential to improve individuals’ life who are dealing with severe conditions like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, or paralysis[46]. This technology creates new opportunities for self-expression and social interaction, thereby enriching the overall well-being of individuals facing these challenges.

3.2. General Medical Applications

In addition to transforming healthcare through advanced neurological assessment and understanding of cognitive processes, Brain-Computer Interface (BCI) technology has also made remarkable advancements in several other medical applications as well. BCI offers innovative solutions which are being used in understanding cognitive process and providing new equipment for neurological assessment [47,48,49]. In the realm of general medical applications, significant strides have been developed for patients with neurological disorder, patients who are non-verbal or paralyzed, have severe attack from stroke and traumatic brain injuries [50,51,52,53].

3.2.1. Medication Optimization

In medication optimization, BCI technology involve in evaluating the efficacy and potential adverse reactions of medications. By analyzing the brain activity patterns, BCI offers insights into patients’ response to various medications which might facilitate personalized treatment adjustment and refinement of drug regimens [54,55]. Borgheai, Zisk [53] proposed a predictive model which used multimodal BCI system with near-infrared spectroscopy (fNIRS) and EEG. Such predictive models could obtain a R-2 values of maximum 0.942 with an average performance gain of 5.18%. Other BCI models in medication optimization include Pharmacovigilance BCI (PV-BCI), Pharmacological Neuroimaging BCI (PN-BCI) and Pharmacodynamic Response BCI (PR-BCI) [56].

3.2.2. Pain Management

BCIs contribute to pain management by monitoring neural signals associated with pain perception. This information can be used to develop personalized pain management strategies, including targeted drug delivery or neurostimulation techniques, to alleviate pain and improve patient comfort [57,58,59].
Furthermore, BCIs can facilitate the implementation of neurostimulation techniques for pain management. Neurostimulation methods, such as spinal cord stimulation or transcranial magnetic stimulation, modulate neural activity to alleviate pain [60]. BCIs provide real-time feedback on pain levels, allowing for precise adjustments to the parameters of neurostimulation devices to optimize pain relief for individual patients.

3.2.3. Surgical Planning

In surgical planning, BCIs aid in preoperative assessments by mapping brain activity to identify critical functional areas and potential risks during surgery [61]. This information guides surgical strategies, minimizes risks, and enhances surgical outcomes by ensuring precise and individualized treatment plans [62,63]. In addition to aiding preoperative assessment, BCI in surgical planning has significant contribution by providing real-time feedback on brain activity patterns. Such feedback allows surgeons to adjust their methods dynamically, ensuring the preservation of critical functional areas and reducing the risk of intraoperative complications during surgery.

3.2.4. Sleep Disorder Monitoring

Brain activity patterns during sleep can be analyzed. By monitoring neural signals associated with sleep stages and disturbances, BCIs can provide objective data to diagnose sleep disorders, assess treatment effectiveness, and inform personalized sleep management interventions [64]. In one such study, Zhang, Mo [65] designed a novel sleep disorder treatment system utilizing the transcranial microcurrent stimulation. Key technical specifications include adjustable stimulation frequencies of 0.5 Hz, 1.5 Hz, and 100 Hz with two-phase constant current stimulation and continuous adjustment of stimulation currents ranging from 0 to 1 mA. Another established method for diagnosing obstructive sleep apnea (OSA) is Polysomnography (PSG). Lin, Prasad [66] have devised a polysomnography system tailored for comprehensive sleep monitoring purposes. It is essential to note that BCI-based sleep monitoring system implementation is still in its early stages, and further research and development are needed to validate its effectiveness and reliability [67].

3.2.5. Human-Computer Interaction (HCI)

BCIs revolutionize human-computer interaction by enabling direct communication between the brain and computer systems. This technology allows users to control computers, devices, and interfaces solely through brain signals, offering a novel and intuitive interaction method for individuals with physical disabilities or limitations [68,69]. In this context, Sharma [70] introduced a Multi-Label Sequential Convolutional Neural Network (EM-LSCNN) designed for identifying the facial landmarks of the given face. Upon implementation and fine-tuning according to the user, this model alters the movement of the mouse indicator across the screen’s viewport, eliminating the necessity for a physical mouse. The proposed model exhibited outstanding performance metrics, achieving an accuracy of 98.85%, a precision of 99.20%, an f1-score of 98.65% and a recall of 98.30. In another HCI related study, Siow, Chew [71] designed a prototype enabling users to manipulate the cursor by translating real-time synaptic commands. An EEG data collection session was conducted, during which experimental subjects underwent training to master the manipulation of the EMOTIV Insight.

3.2.6. Communication Assistance

BCIs provide communication assistance for individuals with speech or communication impairments by translating brain signals into text or speech output. This technology allows non-verbal individuals to communicate effectively, fostering independence, social interaction, and improved quality of life [72,73,74]. Zhou, Yu [75] proposed a collaborative robotic arm control system integrating hybrid asynchronous Brain-Computer Interface (BCI) and computer vision technologies. This model merges steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, enabling users to select from fifteen commands asynchronously, dictating robot actions within a 3D workspace and reaching targets across a broad movement spectrum. Concurrently, computer vision capabilities are leveraged to identify objects and aid the robotic arm in executing more precise tasks, including automated target grasping.
In another study, Pooya Chanu, Pei [76] explored electroencephalogram (EEG)-based control of a prosthetic hand. A support vector machine (SVM) has been utilized in conjunction with 24-fold cross-validation to classify extracted features. To optimize SVM hyperparameters, a Bayesian optimizer was employed, with a minimum prediction error serving as the objective function. This study showcases the feasibility of utilizing EEG for controlling a prosthetic hand by individuals with motor neuron disabilities.

3.3. Psychology or Neurology

BCI applications have promising potentials in addressing and managing a various range of neurological conditions. From detecting Alzheimer diseases, understanding emotional states, providing deeper insights about therapeutic approaches to diagnosing mental health, BCI has numerous applications[77,78].

3.3.1. Alzheimer Disease Treatment

Machine learning algorithms are used to develop various predictive models to decode EEG features, classify information, and provide tailored feedback to the user while assisting neurological disease[79,80]. Psychological factors such as motivation, attention, and frustration also play an important role in human-machine interaction. da Silva-Sauer, Torre-Luque [81] evaluated the usefulness of Brain-Computer Interface (BCI) systems in the cognitive rehabilitation and neuroplasticity promotion of people with dementia. The study involved a total of 10 volunteers with mild to moderate dementia. The tasks included in the study were a motor imagery task, a visual oddball task, and a P300 speller task. In a similar study, Martínez-Cagigal, Santamaría-Vázquez [82] created an asynchronous BCI system centered on P300, enabling users to command Twitter and Telegram on an Android device. In this study, the row-col paradigm (RCP) is employed to stimulate P300 potentials on the user’s scalp, which are promptly processed for decoding the user’s intentions with motor-disabled individuals.

3.3.2. Depression

BCI provides individual with real-time feedbacks on brain activities which might allow them to engage in neurofeedback training [83]. In one study, Widge, Malone Jr [84] introduced a technology lifecycle framework, indicating that initial trial setbacks result from excessive enthusiasm for an emerging technology. They also suggested that Deep Brain Stimulation (DBS) might be approaching a phase of significant advancement by merging recent mechanistic discoveries with the maturation of Brain-Computer Interface (BCI) technology. In another study, Liao, Wu [85] developed a machine learning algorithm that can accurately detect major depression from EEG signals. The authors aimed to use the Kernel Eigen-Filter-Bank Common Spatial Patterns (KEFB-CSP) algorithm to extract discriminative features from EEG signals and train a classifier to distinguish between depressed and non-depressed individuals.
EEG-based interventions could provide a more personalized and effective approach to managing PSD in stroke patients [86]. The effectiveness of electroencephalography (EEG) in managing post-stroke depression (PSD) and improving rehabilitation outcomes are investigated by Yang, Huang [87]. The study also identified significant differences in EEG measures between depressed and non-depressed patients, highlighting the potential of EEG as a diagnostic tool for PSD.

3.3.3. Epilepsy

EEG-based BCIs have advanced significantly in recent years, with promising applications in various fields such as communication, rehabilitation, and entertainment [88]. Sparse representation-based classification methods have shown great potential in EEG signal processing and can significantly improve the accuracy and efficiency of EEG-based tasks, but more research is needed to fully exploit their benefits, specially while treating epilepsy [89]. The potential of direct electrical stimulation (DES) is investigated as an enabling technology for input to the cortex in electrocorticographic (ECoG) brain-computer interfaces (BCIs) by Caldwell, Ojemann [90]. Authors suggested that DES can provide a means of generating artificial sensory input or modulating cortical activity to improve BCI performance. Moreover, stereotactic EEG (sEEG) plays a vital role in the evaluation and management of epilepsy, particularly in cases where other diagnostic techniques, such as standard EEG or imaging, have yielded inconclusive results [91,92].

3.3.4. Emotion Classification

BCIs are employed to classify and interpret human emotional states based on brain activity patterns [93,94,95,96,97,98]. In this context, Teo and Chia [99] worked on EEG data, preprocessed, and segmented into epochs, which were then used to extract spectral features using the Fast Fourier Transform (FFT) algorithm. The features were fed into a deep learning model, which consisted of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. This study suggested that EEG-based excitement detection using deep learning models could have practical applications in areas such as gaming, marketing, and mental health.
In the context of EEG based- emotion classification, authors proposed a novel approach that combines electroencephalography (EEG) and galvanic skin response (GSR) to capture and classify emotional states. The authors also performed feature selection using a Recursive Feature Elimination (RFE) algorithm to identify the most relevant features for emotion recognition [100]. The results of the study suggest that the proposed BCI system using EEG and GSR data can achieve high accuracy in recognizing emotions in people with visual disabilities. The Random Forest (RF) model achieved the highest accuracy of 84.5% in emotion recognition. Another improved EEG pattern decoding is presented by Zhang, Zhou [101] where one-vs-all encoding is used for propagation-based clustering. Such EEG pattern decoding suggests substantial improvement in brain pattern recognition in BCI.

3.3.5. Seizure

BCI technology has a great promise for the improvement of epilepsy management by enabling early seizure prediction and detection, facilitating targeted neuromodulation therapies, and guiding personalized treatment strategies [102,103]. Devices such as neurostimulation (RNS) can be integrated to deliver targeted electrical stimulation to the brain in response to detected seizure activity. Furthermore, BCIs can automatically detect and classify epileptic seizures in real-time based on patterns of brain activity. This capability allows for timely intervention, such as triggering responsive devices or alerting healthcare providers, to mitigate the impact of seizures and ensure patient safety.

3.3.6. Stress Evaluation

BCI integrated with machine learning models can classify emotional states, including stress. By analyzing different patterns of brain activity, BCI system can identify when and how an individual experience stress and to what extent [103,104]. Such research conducted by Lin, Liu [105] where authors designed a cost-efficient, readily producible, adaptable, durable, and gel-free electroencephalogram (EEG) electrode using a combination of silver nanowires, polyvinyl butyral, and a melamine sponge (AgPMS). This innovative electrode overcomes issues associated with hair interference. Through the silver nanowires’ surface metallization, the sponge maintains a high conductivity of 917 S/m without any increase in weight. In another study, Khosrowabadi, Quek [106] suggested a BCI system which can be applied to categorize the participants’ mental stress level based on the features extracted from their EEG signal data. These features include Gaussian mixtures of EEG spectrogram, Higuchi’s fractal dimension of EEG, and Magnitude Square Coherence Estimation (MSCE) among EEG channels. Using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms, classification of these EEG features is carried out.

3.3.7. Cognitive Impairment

By providing alternative means of communication and interaction, BCI technology can assist patients with severe cognitive impairment. In this way, they can express themselves and engage with their environment using brain signals to control communication devices [107,108,109,110]. BCI-based cognitive training tasks, such as memory exercises, attention training, and executive function challenges, provide targeted cognitive stimulation and feedback tailored to individual requirements.

3.3.8. Neuropsychiatric Disorder

Neuropsychiatric disorder encompasses a range of mental health conditions, including schizophrenia, bipolar disorder, and major depressive disorder. BCI-based neurofeedback techniques may offer novel therapeutic approaches for managing symptoms of these disorders [111,112]. Numerous researchers have explored different aspects of BCI technology in the field of neuropsychiatric disorders. One notable development is the creation of an estimation and classification system that considers age and gender factors while utilizing structural magnetic resonance imaging (sMRI) brain images [113]. This system aims to improve the accuracy of diagnosing and classifying neuropsychiatric disorders by incorporating demographic variables and leveraging advanced neuroimaging techniques. By integrating BCI with sMRI data, researchers seek to enhance our understanding of the neural mechanisms underlying these disorders and develop more effective diagnostic and treatment strategies tailored to individual patients.

3.3.9. Anxiety Assessment

Anxiety assessment refers to evaluating and managing anxiety disorders, such as social anxiety disorder, generalized anxiety disorder, and post-traumatic stress disorder. By analyzing patterns of brain activity associated with stress and arousal, BCI can aid in providing the biomarkers of anxiety [114,115,116,117]. The accuracy of anxiety assessment can be enhanced through such BCI enabled designs. Other applications include inform treatment decisions, and facilitate the development of novel interventions, such as BCI-based biofeedback training for anxiety regulation.

3.3.10. Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder marked by challenges in sustaining attention, managing impulses, and regulating activity levels [118,119]. BCIs have the capability to monitor attentional processes in real-time by observing patterns of brain activity associated with attentional engagement and disengagement. BCIs can adjust task parameters or provide motivational cues based on real-time assessments of attentional state, facilitating task completion and goal attainment [120,121]. In one study, Lai, Yu [122] combined Brain-Computer Interface (BCI) technology with Tangible User Interface (TUI) techniques and introduced the prototype of a TUI jigsaw puzzle, named E-Jigsaw, aimed at assisting children with attention deficit disorder and Attention Deficit Hyperactivity Disorder. E-Jigsaw incorporates an interactive design that aligns with neural feedback mechanisms. Its TUI functionality is designed to enhance hand-eye coordination, precise manipulation skills, sensory integration ability, and attention levels through engaging user interactions. Furthermore, for real time attention monitoring, Prabhu, Das [123] introduced a novel smart wearable headband prototype which combines EOG and EEG sensors. This advanced device facilitates the continuous tracking of brain activity and eye movements in real-time during various activities. This capability enables the detection of subtle shifts in alertness, attention, and perception.

3.4. Pediatric Applications

In pediatric healthcare, there are numerous innovative solutions for diagnosis, treatment, and rehabilitation. Cognitive function can be measured through such technology in pediatric patients, assisting clinicians in assessing attention, memory, and executive function skills. This application can be further segmented into pediatric neurorehabilitation and neurodevelopmental monitoring.

3.4.1. Pediatric Neurorehabilitation

Pediatric neurology research is based on investigating brain-behavior relationships, neural mechanisms of development, and biomarkers of neurological disorders in children. By studying brain activity patterns, BCIs enhance our understanding of pediatric neurological conditions, facilitating early diagnosis, intervention, and personalized treatment approaches [124,125]. Hasan, Sattar [112] conducted a study utilizing a potential diagnostic biomarker for Neuropathic Pain (NP). The anticipated insights from this research hold significant clinical relevance in the development of neurofeedback-based neurorehabilitation and connectivity-based brain-computer interfaces for patients with Spinal Cord Injury (SCI).

3.4.2. Neurodevelopmental Monitoring

Neurodevelopmental monitoring entails the methodical observation and evaluation of a child’s neurological and cognitive development over time. This process allows healthcare professionals to track developmental milestones, identify potential delays or disorders, and provide early intervention and support when necessary [126,127]. By monitoring neurological development from infancy through childhood, clinicians can detect and address neurodevelopmental concerns early, promoting optimal outcomes for children’s cognitive, social, and emotional well-being [128,129].

3.5. Personalized Medicine

Personalized Medicine through this technology involves tailoring medical treatments and interventions to individual patients based on their unique neural activity patterns, cognitive abilities, and clinical characteristics. By leveraging BCI technology, healthcare providers can obtain real-time insights into patients’ brain function and neurological status, allowing for more precise diagnoses, treatment plans, and therapeutic interventions [130]. This personalized approach enhances the effectiveness of medical care by accounting for variations in patients’ physiological responses, preferences, and treatment outcomes, ultimately enhancing patient results and overall well-being [131].

3.5.1. Neurofeedback Therapy

Neurofeedback therapy has been used to address a variety of neurological and psychological conditions, including attention deficit ADHD, anxiety, insomnia, traumatic brain injury (TBI), depression, epilepsy, and post-traumatic stress disorder (PTSD) [132,133]. It is also used for peak performance training in athletes, musicians, and other professionals seeking to enhance cognitive function and concentration [134,135].
Tailoring therapy dosages could potentially optimize improvements in motor functions. Bigoni, Beanato [136] developed a therapeutic approach involving two consecutive interventions, continuing until the patient demonstrates no additional motor enhancement, with a minimum of 11 sessions each. The key outcome in this study is defined as a 4-point enhancement in the Fugl-Meyer assessment of the upper extremity, was achieved in the initial patient, showing an elevation from 6 to 11 points between T0 and T2. This progress was accompanied by alterations in the structure and function of the motor network.

3.5.2. Individual Treatment Planning

Individual treatment planning involves customizing medical interventions and therapies to align with the specific needs, characteristics, and preferences of each patient. This approach recognizes that individuals may respond differently to treatments due to factors such as genetics, lifestyle, coexisting conditions, and personal preferences. By customizing treatment plans for each patient, healthcare providers can maximize the effectiveness of interventions, minimize adverse effects, and improve patient outcomes [137,138,139]. Brain-Computer Interface (BCI) technology can have a significant impact on individual treatment planning by providing insights into patients’ neural activity, cognitive function, and real-time responses to interventions [140].

4. Discussion

Brain-computer interface (BCI) technology integration shows considerable potential across a wide array of fields. In the realm of medical applications, BCI technology plays a crucial role in enhancing accessibility and improving outcomes for individuals, especially those with disabilities. By harnessing BCI-controlled assistive devices such as prosthetics, wheelchairs, and communication aids, patients are empowered to regain autonomy and enhance their quality of life, conquering the obstacles to mobility and communication.
Furthermore, BCI-driven neurorehabilitation programs offer tailored therapies for patients recovering from neurological traumas or conditions. These programs facilitate motor recovery, cognitive rehabilitation, and functional independence through personalized interventions. This review work significantly contributes to advancing the understanding and application of Brain-Computer Interface (BCI) technology in medical contexts. By synthesizing and consolidating a diverse range of related research, our review paper provides a comprehensive overview of the field’s current state.
Through systematic segmentation, discussion, and referencing of numerous relevant studies, our review explicates key findings, identifies emerging trends, and highlights gaps in knowledge. By shedding light on the multifaceted applications of BCI technology in healthcare, this work serves as a valuable resource for researchers, clinicians, and stakeholders seeking to harness the full potential of BCI technology in improving patient care and outcomes.

5. Conclusions

BCI technology has introduced new possibilities in medical research, diagnosis, and treatment by enabling direct communication between the brain and external devices. From predictive analytics to neurofeedback therapy, BCI is reshaping the landscape of healthcare, offering unprecedented insights and capabilities. By enabling control over assistive devices such as prosthetics, wheelchairs, and communication aids, BCI technology empowers patients to regain autonomy and quality of life, addressing barriers to mobility and communication. This survey work represents a pivotal milestone in the field of Brain-Computer Interface technology, particularly in its applications within the medical domain by synthesizing a wide array of research findings. This systematic categorizing of the diverse applications of BCI technology, will serve as an indispensable guide for shaping future research directions. And researchers in this field might gain a depth understanding of the current landscape of BCI technology in medical applications. By highlighting key findings, identifying trends, and pinpointing areas for further investigation, this paper offers invaluable insights that can inform the development of innovative BCI-based solutions for healthcare challenges.

Author Contributions

Meenalosini Vimal Cruz—Conceptualization, Methodology, Resource Collection, Writing, Validation, Review, Supervision, project administration. Suhaima Jamal—Investigation, Paper collection, writing and draft preparation, formatting, Analysis, editing, Visualization. Sibi Chakkaravarthy—Review, Proof reading.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Preprints 100359 g001
Figure 1. BCI Medical Application Taxonomy.
Figure 1. BCI Medical Application Taxonomy.
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Figure 2. End to end BCI system.
Figure 2. End to end BCI system.
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