Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification


Toa, Chean Khim and Sim, Kok Swee and Tan, Shing Chiang (2021) Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification. In: 13th International Conference on Computational Collective Intelligence, ICCCI 2021, 29 Sep - 1 Oct 2021, Rhodos, Greece.

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The purpose of this paper is to use the low-cost EEG device to collect brain signal and use the neural network algorithm to classify the attention level based on the recorded EEG data as input. Fifteen volunteers participated in the experiment. The Emotiv Insight headset was used to record the brain signal during participants performing the Visual Attention Colour Pattern Recognition (VACPR) test. The test was divided into 2 tasks namely task A for stimulating the participant to be attentive and task B for stimulating the participant to be inattention. Later, the recorded raw EEG signal passed through a Notch filter and Independent Component Analysis (ICA) to filter out the noise. After that, Power Spectral Density (PSD) was used to calculate the power value of pre-processed EEG signal to verify whether the recorded EEG signal is consistent with the mental state stimulated during task A and task B before performing classification. Since EEG signals exhibit significantly complex behaviour with dynamic and non-linear characteristics, Convolutional Neural Network (CNN) shows great promise in helping to classify EEG signal due to its capacity to learn good feature representation from the signals. An accuracy of 76% was achieved, indicating the feasibility of using Emotiv Insight with CNN for attention level classification.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks (Computer science), Convolution neural network, Electroencephalogram, Emotiv insight, Visual attention colour pattern recognition (VACPR) test
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Engineering and Technology (FET)
Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 04 Nov 2021 04:06
Last Modified: 24 Feb 2023 06:01
URII: http://shdl.mmu.edu.my/id/eprint/9741


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