EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing

Abenna, Said; Nahid, Mohammed; Bouyghf, Hamid; Ouacha, Brahim

doi:10.1016/j.compbiomed.2022.105931

Cited by 18 publications

(8 citation statements)

References 29 publications

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“…During MI, the ERS and ERD phenomena can cause changes in the power spectrum of the μ- and β-rhythms in the EEG signal. 9 Therefore, feature extraction of such energy variations can be performed using spatial filters. The CSP algorithm uses the spatial distribution of the features to project the EEG signal into a subspace and the diagonalisation of the matrix to determine an optimal set of spatial filters for the projection that maximises the difference in variance values between the two types of signals, thus resulting in the most discriminating feature vector.…”

Section: Methodsmentioning

confidence: 99%

“…After inputting the characteristics as real-time discrimination criteria into the receiving module, the system matches the EEG signals received in real time and outputs discrimination results. 9 However, in real-time classification experiments, it is not guaranteed that subjects will always emit the same target EEG signal in a single task; thus, nontarget signals can be considered interference signals that seriously affect the accuracy of real-time classification. To solve the problem of low accuracy in real-time classification, we proposed a label-based channel diversion preprocessing.…”

Section: Eeg Decoder Based On the Channel Selection Fb-trcsp Algorith...mentioning

confidence: 99%

“…8 In addition, to achieve control over external intelligent devices, the accuracy of real-time classification results considerably affects the control results. Abenna et al 9 combined the light gradient boosting machine classifier and the sine cosine algorithm to select the optimal channel and establish the better prediction models, which remarkably improved the accuracy of the binary classification. Jeyabalan et al 10 combined adaptive band pass filters and adaptive autoregressive for classification of left and right MI signals.…”

Section: Introductionmentioning

confidence: 99%

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MI-based BCI with accurate real-time three-class classification processing and light control application

Zhang

Lou

et al. 2023

Proc Inst Mech Eng H

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The use of brain–computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.

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Section: Methodsmentioning

confidence: 99%

Section: Eeg Decoder Based On the Channel Selection Fb-trcsp Algorith...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MI-based BCI with accurate real-time three-class classification processing and light control application

Zhang

Lou

et al. 2023

Proc Inst Mech Eng H

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“…So, they were used to classify motor imagery-brain-computer interface (MI-BCI) on multiple scales. In [23] was conducted to perform binary and multi-class classification on EEG signals for use in real-time BCI applications. So, the outputs of the new real-time approach obtained were discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of electroencephalography using cooperative learning based on participating client balancing

Meqdad

Husain

Jawad

et al. 2023

IJECE

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<span lang="EN-US">Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.</span>

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“…Electrode placement was performed according to the International 10-20 system [34] and the sampling frequency used was 500 Hz. Artefacts in the EEG signals and external interferences were filtered out [35][36][37] using a notch filter at 50 Hz and a low-pass filter with a cut-off frequency of 40 Hz.…”

Section: Materials and Equipmentmentioning

confidence: 99%

Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions

et al. 2022

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Purpose In this paper, a new automated procedure based on deep learning methods for schizophrenia diagnosis is presented. Methods To this aim, electroencephalogram signals obtained using a 32-channel helmet are prominently used to analyze high temporal resolution information from the brain. By these means, the data collected is employed to evaluate the class likelihoods using a neuronal network based on radial basis functions and a fuzzy means algorithm. ResultsThe results obtained with real datasets validate the high accuracy of the proposed classification method. Thus, effectively characterizing the changes in EEG signals acquired from schizophrenia patients and healthy volunteers. More specifically, values of accuracy better than 93% has been obtained in the present research. Additionally, a comparative study with other approaches based on well-knows machine learning methods shows that the proposed method provides better results than recently proposed algorithms in schizophrenia detection. ConclusionThe proposed method can be used as a diagnostic tool in the detection of the schizophrenia, helping for early diagnosis and treatment.

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EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing

Cited by 18 publications

References 29 publications

MI-based BCI with accurate real-time three-class classification processing and light control application

MI-based BCI with accurate real-time three-class classification processing and light control application

Classification of electroencephalography using cooperative learning based on participating client balancing

Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions

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