Towards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integration

In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement classification, utilizing electroencephalogram (EEG) data. These developments are fundamental in exploring how neural signals can be interpreted to recogn...

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Bibliographic Details
Published in:The ... International Winter Conference on Brain-Computer Interface pp. 1 - 4
Main Authors: Kim, Jun-Young, Kim, Deok-Seon, Lee, Seo-Hyun
Format: Conference Proceeding
Language:English
Published: IEEE 24.02.2025
Subjects:
Accuracy
Brain modeling
brain-computer interface
Brain-computer interfaces
classification
Data visualization
Decoding
deep-learning
electroencephalogram
Electroencephalography
Feature extraction
Motors
signal processing
Soft sensors
Symbols
ISSN:2572-7672
Online Access:Get full text
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Summary:In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement classification, utilizing electroencephalogram (EEG) data. These developments are fundamental in exploring how neural signals can be interpreted to recognize specific physical actions. This study centers on a written alphabet classification task, where we aim to decode EEG signals associated with handwriting. To achieve this, we incorporate hand kinematics to guide the extraction of the consistent embeddings from high-dimensional neural recordings using auxiliary variables (CEBRA). These CEBRA embeddings, along with the EEG, are processed by a parallel convolutional neural network model that extracts features from both data sources simultaneously. The model classifies nine different handwritten characters, including symbols such as alphabets, exclamation marks and commas. We evaluate the model using a quantitative five-fold cross-validation approach and explore the structure of the embedding space through visualizations. Our approach achieves a classification accuracy of 91 % for the nine-class task, demonstrating the feasibility of fine-grained handwriting decoding from EEG signals.
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931608