Document Type

Article

Publication Title

IEEE Access

Abstract

Recognizing human movement intention in less controlled environment is the step needed to go beyond strict laboratory settings in order to improve intuitive and reliable human-robot interaction systems. By taking this into account, the present study evaluates the feasibility of decoding the intention to perform hand-arm gestures in self-paced tasks from electroencephalography (EEG) and electromyography (EMG) collected with wearable dry-electrode devices. An experimental protocol was designed to emulate three gestures in human-robot interaction: Neutral, Ask, and Take. The protocol was implemented in two laboratories using different EEG headsets, Ultracortex Mark IV and Muse 2, and a common EMG device, Mindrove armband. Signals were preprocessed and segmented around the start of movement to be classified with a Bidirectional Long Short-Term Memory network with a fully connected output layer (BiLSTM-FC). Although the typical waveform of intention, movement-related cortical potential (MRCP), could not be reliably identified, the results showed that the combination of EEG and EMG improves the discriminability of intention patterns, achieving a mean of 88.95% in accuracy, and a maximum of 95.98% using the Ultracortex Mark IV and the Mindrove armband. These findings demonstrate the robustness of multimodal data gathered with accessible wearable technology to develop hybrid brain-computer interfaces.

First Page

64202

Last Page

642019

DOI

10.1109/ACCESS.2026.3687486

Publication Date

27-4-2026

Language

eng

Rights

open access

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