Frontiers in neuroinformatics · 2025
Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X.
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Tarara Paulina, Przybył Iwona, Schöning Julius, Gunia Artur
Аннотация
INTRODUCTION: Enhancing the command capacity of motor imagery (MI)-based brain-computer interfaces (BCIs) remains a significant challenge in neuroinformatics, especially for real-world assistive applications. This study explores a multiclass BCI system designed to classify multiple MI tasks using a low-cost EEG device.
METHODS: A BCI system was developed to classify six mental states: resting state, left and right hand movement imagery, tongue movement, and left and right lateral bending, using EEG data collected with the Emotiv EPOC X headset. Seven participants underwent a body awareness training protocol integrating mindfulness and physical exercises to improve MI performance. Machine learning techniques were applied to extract discriminative features from the EEG signals.
RESULTS: Post-training assessments indicated modest improvements in participants' MI proficiency. However, classification performance was limited due to inter- and intra-subject signal variability and the technical constraints of the consumer-grade EEG hardware.
DISCUSSION: These findings highlight the value of combining user training with MI-based BCIs and the need to optimize signal quality for reliable performance. The results support the feasibility of scalable, multiclass MI paradigms in low-cost, user-centered neurotechnology applications, while pointing to critical areas for future system enhancement.
METHODS: A BCI system was developed to classify six mental states: resting state, left and right hand movement imagery, tongue movement, and left and right lateral bending, using EEG data collected with the Emotiv EPOC X headset. Seven participants underwent a body awareness training protocol integrating mindfulness and physical exercises to improve MI performance. Machine learning techniques were applied to extract discriminative features from the EEG signals.
RESULTS: Post-training assessments indicated modest improvements in participants' MI proficiency. However, classification performance was limited due to inter- and intra-subject signal variability and the technical constraints of the consumer-grade EEG hardware.
DISCUSSION: These findings highlight the value of combining user training with MI-based BCIs and the need to optimize signal quality for reliable performance. The results support the feasibility of scalable, multiclass MI paradigms in low-cost, user-centered neurotechnology applications, while pointing to critical areas for future system enhancement.