Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs
Mürşide Değirmenci; Yılmaz Kemal Yüce; Matjaž Perc; Yalçın İşler · 2023 · Frontiers in Human Neuroscience
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG
Abstract by Mürşide Değirmenci; Yılmaz Kemal Yüce; Matjaž Perc; Yalçın İşler, Frontiers in Human Neuroscience (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3389/fnhum.2023.1223307