The Classification of Motor Imagery EEG Signals Based on the Time-Frequency-Spatial Feature

Deng, Xin; Zhang, Boxian; Liu, Ke; Wang, Jin; Yang, Pengfei; Hu, Chengxin

doi:10.1109/ddcls52934.2021.9455464

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Discrete Wavelet Transform (DWT) and CSP were utilised in ref. [47] to obtain classification accuracies of 82.86%-89.29%. Several public datasets were used in ref.…”

Section: Eeg-based Systemsmentioning

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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“…Discrete Wavelet Transform (DWT) and CSP were utilised in ref. [47] to obtain classification accuracies of 82.86%-89.29%. Several public datasets were used in ref.…”

Section: Eeg-based Systemsmentioning

confidence: 99%

BIO‐inspired fuzzy inference system—For physiological signal analysis

Suppiah

Kim

Abidi

et al. 2023

IET Cyber-Syst and Robotics

View full text Add to dashboard Cite

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“…The decomposition was computed by repeatedly filtering the discrete signals up to a predefined level. Earlier studies have demonstrated the effectiveness of utilizing Daubechies-4 (order 4) wavelet feature extraction for the classification of different types of EEG signals [38][39][40][41]. In this study, we applied the DWT using the Daubechies-4 wavelet with two-level decomposition on EPOC and the three-level decomposition on MUSE for denoising and information extraction.…”

Section: Eeg Signal Preprocessingmentioning

confidence: 99%

EEG-based classification of imagined digits using a recurrent neural network

Mahapatra

Bhuyan

2023

J. Neural Eng.

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Objective. In recent years, imagined speech brain-computer (machine) interface applications have been an important field of study that can improve the lives of patients with speech problems through alternative verbal communication. This study aimed to classify the imagined speech of numerical digits from electroencephalography (EEG) signals by exploiting the past and future temporal characteristics of the signal using several deep learning models. Approach. This study proposed a methodological combination of EEG signal processing techniques and deep learning models for the recognition of imagined speech signals. The EEG signals were filtered and preprocessed using the discrete wavelet transformation (DWT) to remove artifacts and retrieve feature information. To classify the preprocessed imagined speech neural signals, multiple versions of multlayer bidirectional recurrent neural networks were used. Main results. The method was examined by leveraging MUSE and EPOC signals from MNIST imagined digits in the MindBigData open-access database. The presented methodology’s classification performance accuracy was noteworthy, with the model’s multiclass overall classification accuracy reaching a maximum of 96.18 percent on MUSE signals and 71.60 percent on EPOC signals. Significance. Furthermore, this research shows that the proposed signal preprocessing approach and the stacked bidirectional recurrent network model are suitable for extracting the high temporal resolution of EEG signals in order to classify imagined digits, indicating the unique neural identity of each imagined digit class that distinguished it from the others.

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