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• W4200454076 abstract "Responses of the human brain to different visual stimuli elicit specific patterns in electroencephalography (EEG) signals. It is confirmed that by analyzing these patterns, we can recognize the category of the visited objects. However, high levels of noise and artifacts in EEG signals and the discrepancies between the recorded data from different subjects in visual object recognition task make classification of cognitive states of subjects a serious challenge. In this research, we present a framework for evaluating machine learning and wrapper channel selection algorithms used for classifying single-trial EEG signals recorded in response to photographic stimuli. It is shown that by correctly mapping the entire EEG data space to informative feature spaces (IFS), the performance of the classification methods can improve significantly. Results outperform the state-of-the-art results and confirm efficiency of the proposed feature selection methods in capturing the most informative EEG channels. This can help to achieve high separability of object categories in single-trial visual object recognition task." @default.
• W4200454076 created "2021-12-31" @default.
• W4200454076 creator A5044447124 @default.
• W4200454076 creator A5067238434 @default.
• W4200454076 date "2022-07-01" @default.
• W4200454076 modified "2023-09-30" @default.
• W4200454076 title "Visual Object Recognition From Single-Trial EEG Signals Using Machine Learning Wrapper Techniques" @default.
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• W4200454076 doi "https://doi.org/10.1109/tbme.2021.3138157" @default.
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