Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces
원문 초록
Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to reconstruct speech from neural activity. However, decoding communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in EEG data from 16 participants. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Personalized calibration is crucial for optimizing BCI performance.
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AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP Tasks
Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers
토론
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