@inproceedings{af4647fb6a06487fbe0ca979483f89be,
title = "Finger flexion imagery: EEG classification through physiologically-inspired feature extraction and hierarchical voting",
abstract = "Accurate electroencephalography (EEG) classification of finger flexion imagery would endow non-invasive brainmachine interfaces (BMIs) with a much richer control repertoire. Traditionally, it has been assumed that non-invasive methods lack the resolution required for success on such a fine discrimination task. In this study, we challenged this assumption. EEG was acquired while subjects imagined performing individual and bimanual finger flexions. A new method of spatiotemporal and spectral feature extraction was applied, and multi-class support vector machine (SVM) classifiers were trained. Predictions and probabilities then served as inputs to a novel voting scheme, which output the system decision. The present approach achieved a mean population (n=15) accuracy of 30.86±1.76%, nearly twice the chance guessing level (16.71±1.68%) for the six-class task evaluated. Finger imagery is thus shown to be classifiable through EEG analysis alone.",
keywords = "BMI, Decision system, EEG, Motor imagery, Multi-class SVM, Spatiotemporal features, Spectral features",
author = "Daniel Furman and Roi Reichart and Hillel Pratt",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th International Winter Conference on Brain-Computer Interface, BCI 2016 ; Conference date: 22-02-2016 Through 24-02-2016",
year = "2016",
month = apr,
day = "20",
doi = "10.1109/IWW-BCI.2016.7457445",
language = "אנגלית",
series = "4th International Winter Conference on Brain-Computer Interface, BCI 2016",
booktitle = "4th International Winter Conference on Brain-Computer Interface, BCI 2016",
}