Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

Sreeja, S. R.; Samanta, Debasis; Mitra, Pabitra; Sarma, Monalisa

doi:10.5455/jjcit.71-1512555333

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…The method was based on the estimation of true covariance matrices of each motor imagery task. In another study, Sreeja et al [50] revealed that selection of 30 electrodes placed on premotor cortex, supplementary motor cortex, and primary motor cortex in combination with Despite the demonstrated possibility of optimization techniques to significantly increase classification accuracy, they strongly depend on initial data features, which along with motor-related features include other patterns related to individual subject characteristics that require preliminary calibration of the classifier for each subject. According to this fact, in our study, we propose the optimization method based on the motor-related EEG features based on the spatiotemporal and time-frequency EEG analysis in the group of subjects.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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“…However, when assessing each applicant feature subset, the wrapper method must train and test the classifier, and that is prone to overfitting and computationally costly. The embedded technique incorporates feature selection into the classifier's training phase, performing both tasks concurrently [36,37]. Although proven to be efficient in most studies, the embedded technique has the highest computational cost in all three feature selection methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

et al. 2021

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Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

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Classification of Left/Right Hand and Foot Movements from EEG using Machine Learning Algorithms

Cherifi,

Berghouti,

Boubchir

2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach

Abstract: ABSTRACT

Cited by 6 publications

References 31 publications

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Classification of Left/Right Hand and Foot Movements from EEG using Machine Learning Algorithms

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