Classification of EEG signal using EACA based approach at SSVEP-BCI
| Autor: | H. C. Nagaraj, S R Ashwini |
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| Rok vydání: | 2021 |
| Předmět: |
Brain computer interfaces
Information Systems and Management Steady state (electronics) medicine.diagnostic_test Computer science business.industry Brain activity and meditation Interface (computing) Feature extraction Process (computing) Pattern recognition Electroencephalography Identification (information) Event based adaptive component approach Information transfer rate Artificial Intelligence Control and Systems Engineering medicine Artificial intelligence Electrical and Electronic Engineering business Steady state visually evoked potentials Brain–computer interface |
| Popis: | The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach. |
| Databáze: | OpenAIRE |
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