Multiclass Classification of Spatially Filtered Motor Imagery EEG Signals Using Convolutional Neural Network for BCI Based Applications

Shajil, Nijisha; Mohan, Sasikala; Srinivasan, Padmini; Arivudaiyanambi, Janani; Murrugesan, Arunnagiri Arasappan

doi:10.1007/s40846-020-00538-3

Cited by 38 publications

(22 citation statements)

References 27 publications

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“…Furthermore, the Greenhouse–Geisser correction was applied for p -value adjustment. Using the statistical software G * Power of the given parameters setting and referring to some existing studies (Zheng et al, 2019 ; Shajil et al, 2020 ; Cao et al, 2021 ), the sample size is 16 subjects in this study.…”

Section: Methodsmentioning

confidence: 99%

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Liu

et al. 2022

Front. Neurosci.

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Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain–computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.

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Section: Methodsmentioning

confidence: 99%

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Liu

et al. 2022

Front. Neurosci.

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“…Automatic feature extraction from raw data, without the need for prior knowledge and without the need for an expert or a specialist, as well as the concept of hierarchical learning in it, has made it essential to do this work. It has been shown that in the early layers, low-level features are learned, and as it deepens, higher level concepts are learned [ 20 , 23 ]. In this model, Dropout and max-pooling layers are used to prevent overfitting [ 24 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Epilepsy seizure prediction with few-shot learning method

et al. 2022

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Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them. In the previous works, generalized methods were inevitably used for this group of patients which were not very accurate. Our approach to solve this problem is to provide a few-shot learning method. This method, having the previous knowledge, is trained with only a small number of samples, learns new tasks and reduces the efforts to collect more data. Evaluation results for three patients from the CHB–MIT database, for a 10-min seizure prediction horizon (SPH) and a 20-min seizure occurrence period (SOP), averaged sensitivity of 95.70% and a false prediction rate (FPR) of 0.057/h and for the 5-min prediction horizon and the 25-min seizure occurrence period averaged sensitivity of 98.52% and a false prediction rate of (FPR) of 0.045/h. The proposed few-shot learning method, based on previous knowledge gained from the generalizable method, is regulated with a few new patient samples for the patient. Our results show that the accuracy obtained in this method is higher than the generalizable methods.

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“…This contralateral activation, extracted from time and frequency bands over the sensorimotor cortex region, must be identified correctly to evaluate MI recognition's (in)efficiency [40,41]. Consequently, additional efforts are to be further conducted to improve the CNN-based training, aiming at better explaining spectral, temporal, and spatial behavioral patterns that act as constraints/guidelines in interpreting motor imagery skills [42,43].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Collazos-Huertas

Álvarez-Meza

Castellanos-Domínguez

2022

Applied Sciences

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Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions.

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Multiclass Classification of Spatially Filtered Motor Imagery EEG Signals Using Convolutional Neural Network for BCI Based Applications

Cited by 38 publications

References 27 publications

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Epilepsy seizure prediction with few-shot learning method

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

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