A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

Orban, Mostafa; Elsamanty, Mahmoud; Guo, Kai; Zhang, Senhao; Yang, Hongbo

doi:10.3390/bioengineering9120768

Cited by 43 publications

(16 citation statements)

References 104 publications

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“…While there have been many survey articles published on EEG signal processing, these articles often focus on general aspects of the field and do not provide a comprehensive overview of EEG for medical diagnosis. In 2022, Mosfafa Orban et al [5] provided a comprehensive review of EEG; however, it also discusses the development of natural interaction strategies, with a specific emphasis on EEG recording, preprocessing, classification of diseases, and control strategies. It does not provide insight for EEG in medical healthcare and diagnosis.…”

Section: A Main Contributions Of This Studymentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer's, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.

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Section: A Main Contributions Of This Studymentioning

confidence: 99%

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

Amer,

Belhaouari

2023

IEEE Access

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“…Electroencephalography (EEG) -voltage fluctuations resulting from ionic current within the neurons of the brain [1]-has been a key tool in the field of neuroscience, unraveling new opportunities and paving the way for the newly-established field of braincomputer Interfaces (BCIs). By definition, a BCI is a digital system that allows a direct communication of the brain with the external world while bypassing normal communication pathways (like muscles and nerves).…”

Section: Introductionmentioning

confidence: 99%

A causal perspective on brainwave modeling for brain–computer interfaces

Barmpas,

Panagakis,

Zoumpourlis

et al. 2024

J. Neural Eng.

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Machine learning models have opened up enormous opportunities in the field of Brain-Computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one’s viewpoint on some of these major challenges which can be found in various stages in the machine learning pipeline, ranging from data collection and data pre-processing to training methods and techniques. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs. Furthermore, we present how general machine learning practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques’ performance and efficiently compare them with other methods that will be developed in the future.

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“…Brain-computer interfaces (BCIs) utilize users’ brain activity to control external devices without the need of actual movements ( Wolpaw et al, 2002 ; Belkacem et al, 2020 ). This brain activity can be recorded using electroencephalography (EEG), electrocorticography, stereo electroencephalography, functional near-infrared spectroscopy (fNIRS) or functional magnetic resonance imaging (fMRI), with EEG being used the most ( Orban et al, 2022 ; Islam and Rastegarnia, 2023 ). Recently, BCIs using EEG have emerged as a promising technology for UE motor rehabilitation after stroke ( Mane et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system

Sieghartsleitner,

Sebastián-Romagosa,

Cho

et al. 2024

Front. Neurosci.

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IntroductionBrain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.MethodsPatients’ functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.ResultsPatients’ motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.DiscussionThe results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.

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A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

Cited by 43 publications

References 104 publications

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review

A causal perspective on brainwave modeling for brain–computer interfaces

Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system

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