Classification of right and left-hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy

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EBRAHIMI HAMID; Shalbaf Ahmad; Jafarnia Dabanloo Nader

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Journal Paper

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Journal: ADVANCES IN COGNITIVE SCIENCE Year:2020 | Volume:22 | Issue:3 Page(s): 95-104

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Information Journal Paper

Title

Classification of right and left-hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy

Author(s)

EBRAHIMI HAMID | Shalbaf Ahmad | Jafarnia Dabanloo Nader | Issue Writer Certificate

Keywords

Brain-computer interfaceQ1

ElectroencephalographyQ1

Near-infraredQ1

light spectroscopyQ1

Convolutional neural networkQ1

Abstract

Introduction: In this study, a hybrid Brain-computer interface for the classification of right and left-hand motor imagery using the deep learning method is presented to increase accuracy and performance. A hybrid Brain-computer interface is designed to achieve a way of communicating between the brain and an external device for patients such as amyotrophic lateral sclerosis. Therefore, the user can control the external device such as a wheelchair without using any organs of the body and only using the brain. Methods: Two electroencephalographic and Near-infrared spectroscopy signals were recorded from 29 healthy men and women, and pre-processing of the signals was done to eliminate noise. The wavelet transform was used to obtain the scalogram as two-dimensional images for both of the signals, and images were inserted separately from each region of the brain and the merge region into the pre-trained Convolutional neural network to extract features, classification, and prediction of left and right-hand motor imagery. Results: The results for a combination of scalogram images of Frontal-Central and Central-Parietal regions in electroencephalographic signal reached 88%, for Near-infrared light spectroscopy reached 85% and for the merge of two scalogram images reached 90%. Conclusion: The combination of scalogram images and the deep learning method used in this study reached significant improvement in the prediction accuracy of right and left-hand motor imagery for wheelchair motion control.

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APA: Copy

EBRAHIMI, HAMID, Shalbaf, Ahmad, & Jafarnia Dabanloo, Nader. (2020). Classification of right and left-hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy. ADVANCES IN COGNITIVE SCIENCE, 22(3 ), 95-104. SID. https://sid.ir/paper/393313/en

Vancouver: Copy

EBRAHIMI HAMID, Shalbaf Ahmad, Jafarnia Dabanloo Nader. Classification of right and left-hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy. ADVANCES IN COGNITIVE SCIENCE[Internet]. 2020;22(3 ):95-104. Available from: https://sid.ir/paper/393313/en

IEEE: Copy

HAMID EBRAHIMI, Ahmad Shalbaf, and Nader Jafarnia Dabanloo, “Classification of right and left-hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy,” ADVANCES IN COGNITIVE SCIENCE, vol. 22, no. 3 , pp. 95–104, 2020, [Online]. Available: https://sid.ir/paper/393313/en

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