A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

ElSayed, Nesma E.; Tolba, A. S.; Rashad, M. Z.; Belal, Tamer; Sarhan, Shahenda

doi:10.1109/access.2021.3097797

Cited by 19 publications

(4 citation statements)

References 57 publications

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“…For example, we can recognize features representing an instruction for moving a robotic arm. This component is usually implemented using machine learning and classification methods [ 52 – 54 ].…”

Section: Fundamental Components Of Bci Systemmentioning

confidence: 99%

Brain–computer interface: trend, challenges, and threats

Maiseli,

Abdalla,

Massawe

et al. 2023

Brain Inf.

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Brain–computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.

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Section: Fundamental Components Of Bci Systemmentioning

confidence: 99%

Brain–computer interface: trend, challenges, and threats

Maiseli,

Abdalla,

Massawe

et al. 2023

Brain Inf.

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“…In this work, we processed each of the four datasets separately, taking the fourth dataset as our example to show the processing pipeline. We then compared the CNN-based framework with two other state-of-the-art methods, i.e., SVM-based method [29] and DNN-based framework [30,31].…”

Section: Data Processingmentioning

confidence: 99%

Deep Convolutional Neural Network for EEG-Based Motor Decoding

et al. 2022

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Brain–machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.

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“…A DBN was used in ref. [44] to investigate different types of input stimuli and their role in classification accuracies. Using the BCI Competition Dataset IVb, the authors in ref.…”

Section: Introductionmentioning

confidence: 99%

BIO‐inspired fuzzy inference system—For physiological signal analysis

Suppiah

Kim

Abidi

et al. 2023

IET Cyber-Syst and Robotics

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When a person's neuromuscular system is affected by an injury or disease, Activities‐for‐Daily‐Living (ADL), such as gripping, turning, and walking, are impaired. Electroencephalography (EEG) and Electromyography (EMG) are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject, and they are used in Brain–Computer Interface (BCI) or robotic rehabilitation systems. However, existing BCI or robotic rehabilitation systems use signal classification technique limitations such as (1) missing temporal correlation of the EEG and EMG signals in the entire window and (2) overlooking the interrelationship between different sensors in the system. Furthermore, typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions; (3) their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals. A novel classification model, named BIOFIS is proposed, which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships. It explores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory (LSTM) block. The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward, grip and raise, lower and release, and reverse. The system can achieve 98.6% accuracy for a 4‐way action using EEG data and 97.18% accuracy using EMG data. Moreover, even without the dominant signal, the accuracy scores were 90.1% for the EEG data and 85.2% for the EMG data. The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

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A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

Cited by 19 publications

References 57 publications

Brain–computer interface: trend, challenges, and threats

Brain–computer interface: trend, challenges, and threats

Deep Convolutional Neural Network for EEG-Based Motor Decoding

BIO‐inspired fuzzy inference system—For physiological signal analysis

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