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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| Vydáno v: | The ... International Winter Conference on Brain-Computer Interface s. 1 - 4 |
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24.02.2025
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| ISSN: | 2572-7672 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Kim, Jun-Young Kim, Deok-Seon Lee, Seo-Hyun |
| Author_xml | – sequence: 1 givenname: Jun-Young surname: Kim fullname: Kim, Jun-Young email: j_y_kim@korea.ac.kr organization: Korea University,Dept. of Artificial Intelligence,Seoul,Republic of Korea – sequence: 2 givenname: Deok-Seon surname: Kim fullname: Kim, Deok-Seon email: deokseon_kim@korea.ac.kr organization: Korea University,Dept. of Artificial Intelligence,Seoul,Republic of Korea – sequence: 3 givenname: Seo-Hyun surname: Lee fullname: Lee, Seo-Hyun email: seohyunlee@korea.ac.kr organization: Korea University,Dept. of Brain and Cognitive Engineering,Seoul,Republic of Korea |
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| Snippet | In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement... |
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| SubjectTerms | 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 |
| Title | Towards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integration |
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