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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Artificial Intelligence Research
Tadayasu Komura
Tokyo City University
Computer Science & IT Electrical & Electronics Engineering

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Energy extraction method for EEG channel selection Hilman Fauzi; M. Abdullah Azzam; Mohd. Ibrahim Shapiai; Masaki Kyoso; Uswah Khairuddin; Tadayasu Komura
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.12805

Abstract

Channel selection is an improvement technique to optimize EEG-based BCI performance. In previous studies, many channel selection methods—mostly based on spatial information of signals—have been introduced. One of these channel selection techniques is the energy calculation method. In this paper, we introduce an energy optimization calculation method, called the energy extraction method. Energy extraction is an extension of the energy calculation method, and is divided into two steps. The first step is energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to calculate channel energy, while in the energy selection method we propose three techniques: “high value” (HV), “close to mean” (CM), and “automatic”. All proposed framework schemes for energy extraction are applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement) and motor imagery (imagination of left and right hand movement) were used. The system used a Common Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify the signal features with k = 3. Based on the test results, all schemes for the proposed energy extraction method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using the CM energy selection method was found to be the best channel selection technique.
Defining Common Inter-Session and Inter-Subject EEG Channels Using Spatial Selection Method Hilman Fauzi; Tadayasu Komura; Masaki Kyoso; Mohd. Ibrahim Shapiai; Yasmin Mumtaz
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.968 KB) | DOI: 10.29099/ijair.v6i2.284

Abstract

Redundancy of information on brain signals can lead to reduce brain-computer interface (BCI) performance in applications. To overcome this, EEG channel selection is performed to reduce and/or eliminate a number of channels with irrelevant information. In the previous studies, there is energy calculation methods that have been proposed to perform EEG channel selection to improve BCI performance in classifying the brain command of motor imagery stimulation. In this study, channel selection scheme on motor movement signal will be experimented by using spatial selection method. This study performs the common active channel mechanism that divided into two parts: 1) common active channels between sessions, which known as common Inter-session channels and common active channels. These two techniques can be used by all subjects to interpret motor movement type known as common Inter-subject channels. In order to validate the performance of the proposed framework, CSP (common spatial pattern) is used as a feature extraction method and k-NN with k = 3 as the classification method. The obtained results shows that the proposed channel selection technique is able to choose common active channels in five combination numbers on Inter-sessions and Inter-subjects of the acquired EEG signals. Both types of common active channels are proven to improve BCI performance with an accuracy increase of up to 66%.
Co-Authors Hilman Fauzi, Hilman M. Abdullah Azzam Masaki Kyoso Mohd. Ibrahim Shapiai Uswah Khairuddin Yasmin Mumtaz