Mangayarkarasi, T. and Umamaheswari, B. and Madhumita, S. and Binti Yahya, N. (2023) Classification of Motor Imagery EEG Using Functional Connectivity. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Brain-computer interfaces (BCIs) are a technology that offers the consumer an opportunity manner of communicating with computers. Implementation of a BCI system involves measuring the electrical activity of the brain (EEG) and converting it into output commands. EEG with the mental simulation of hand movements (Motor Imagery EEG) can be used for BCI. In this proposed work the recorded Motor Imagery EEG of several subjects is taken as a database, and the signals are pre-processed by applying filters. Common Spatial Patterns (CSP) and CSPL are extracted. By varying the filter value from 5-15 Hz, for a single subject, 150 samples were recorded from 15 channels with a minimum of 14 features taken as input to the Classification Learner. Optimizable SVM is used for Classification. The classification accuracy is high at 76 when the filter value is 7. © 2023 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 0; Conference of 2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023 ; Conference Date: 14 December 2023 Through 15 December 2023; Conference Code:198193 |
Uncontrolled Keywords: | Brain; Image classification; Support vector machines, Brain-computer interface; Classification accuracy; Common spatial patterns; Electrical activities; Functional connectivity; Hands movement; Interface system; Mental simulation; Motor imagery EEG; Optimizable SVM, Brain computer interface |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:11 |
Last Modified: | 04 Jun 2024 14:11 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/18924 |