Classification of left and right hand motor imagery EEG signals by using deep neural networks

Nuri KORHAN; Leyla ABILZADE; Taner ÖLMEZ; Zümray Dokur ÖLMEZ

doi:10.18100/ijamec.995022

Research ArticlesverifiedPeer-ReviewedOpen Access

Pages: 85-90

Classification of left and right hand motor imagery EEG signals by using deep neural networks

Nuri KORHAN fingerprint•

Leyla ABILZADE fingerprint•

Taner ÖLMEZ fingerprint•

Zümray Dokur ÖLMEZ fingerprint

Publication DateDecember 31, 2021

Volume / IssueVol. 9, No. 4 (pp. 85-90)

DOI Identifier10.18100/ijamec.995022

subject

Abstract

The brain-computer interface (BCI) is one of the most promising technologies that allows us to establish a relationship between brain and devices. In this study, three-channel EEG signals collected from nine subjects performing two motor imagery tasks are classified using two different deep neural network (DNN) based approaches called framework 1 (FW1) and framework 2 (FW2). The proposed frameworks were evaluated using BCI Competition IV-IIb dataset. In FW1, the raw EEG data is directly presented to the deep neural network without performing any pre-processing. In FW2, the EEG data is first filtered with five band pass filters with fifth order (Butterworth), then the common spatial patterns (CSP) method, which introduces additional pseudo channels, is applied to the filtered signals. Two experiments were conducted for each framework. In the first experiment, a unique DNN is trained for each subject, and in the second experiment only one DNN is trained with the combination of training sets of all subjects. The performance of the two experiments are then compared in terms of average accuracy. According to the simulation results, we did not observe a significant difference between the average classification accuracies obtained with the first and the second experiments. Therefore, we concluded that, by the use of DNNs we do not need to train several subject-specific networks which requires high computational loads. On the other hand, we observed that the average classification performance significantly improves by the filtering and extracting features with CSP pre-processes.

Keywords:Common Spatial PatternsConvolutional Neural NetworkDeep LearningEEGMotor imagery

Author Affiliations

Nuri KORHAN — İSTANBUL TEKNİK ÜNİVERSİTESİfingerprint0000-0003-4351-2885
Leyla ABILZADE — İSTANBUL TEKNİK ÜNİVERSİTESİfingerprint0000-0002-6114-904X
Taner ÖLMEZ — İSTANBUL TEKNİK ÜNİVERSİTESİfingerprint0000-0001-6124-2394
Zümray Dokur ÖLMEZ — İSTANBUL TEKNİK ÜNİVERSİTESİfingerprint0000-0001-7660-3236

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Classification of left and right hand motor imagery EEG signals by using deep neural networks

Abstract

Author Affiliations

References (19)

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