Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques

Hassan, Fatima; Hussain, Syed Fawad

doi:10.1007/978-3-031-23239-8_7

Advances in Non-Invasive Biomedical Signal Sensing and Processing With Machine Learning 2023

DOI: 10.1007/978-3-031-23239-8_7

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Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques

Fatima Hassan

Syed Fawad Hussain

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2024

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Cited by 4 publications

References 63 publications

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Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Yun

2024

JYMS

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Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

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Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Yun

2024

JYMS

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Dual Optimization of Deep CNN for Motor Imagery EEG Tasks Classification

Al-Saegh,

Daood,

Ismail

2024

DJES

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The motor imagery (MI) electroencephalographic (EEG) signals are leveraged as control commands to numerus engineering applications, such as operating a wheelchair through thought alone. EEG signals are characterized by their non-stationary nature and high dimensionality, posing noteworthy challenges for building robust identification models with limited data samples. The renewed deep convolutional neural network (CNN) has been harnessed in various fields due to its ability to autonomously extract and select latent features addressing problems across various types of EEG signals. However, CNN architectures comprise a vast parameter and hyperparameter space. Normally, parameters are optimized via the back-propagation algorithm, while hyperparameters are adjusted through a lengthy trial-and-error process. This work mitigates the aforementioned problems by employing the genetic algorithm (GA) as an artificial intelligence optimization tool for optimizing crucial architectural hyperparameters including the number of convolution filters, size of filters, and dropout probability. Furthermore, the two parameters of cropping augmentation, used for boosting the EEG training samples, are also optimized by the GA. The proposed model in this paper (GACNN) that includes dual optimization, achieved both by the back-propagation algorithm and GA, has attained promising results in the analysis of MI EEG signals. Experiments are performed on the known brain-computer interface competition IV 2a dataset (BCIC IV 2a). GACNN has recorded an increment of about 17% in Cohen’s kappa coefficient and a decrement of about 30% in standard deviation (SD) in comparison to state-of-the-art studies.

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Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation

AlQahtani,

Al-Naib,

Althobaiti

2024

Front. Bioeng. Biotechnol.

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The global rise in lower limb amputation cases necessitates advancements in prosthetic limb technology to enhance the quality of life for affected patients. This review paper explores recent advancements in the integration of EEG and fNIRS modalities for smart lower prosthetic limbs for rehabilitation applications. The paper synthesizes current research progress, focusing on the synergy between brain-computer interfaces and neuroimaging technologies to enhance the functionality and user experience of lower limb prosthetics. The review discusses the potential of EEG and fNIRS in decoding neural signals, enabling more intuitive and responsive control of prosthetic devices. Additionally, the paper highlights the challenges, innovations, and prospects associated with the incorporation of these neurotechnologies in the field of rehabilitation. The insights provided in this review contribute to a deeper understanding of the evolving landscape of smart lower prosthetic limbs and pave the way for more effective and user-friendly solutions in the realm of neurorehabilitation.

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Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques

Cited by 4 publications

References 63 publications

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Dual Optimization of Deep CNN for Motor Imagery EEG Tasks Classification

Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation

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