Brain-Computer Interface

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תובנה - Brain-Computer Interface

EEG 기반 폐루프 근육 자극을 통한 행위 주체성 유지

사용자의 의도와 일치하는 근육 자극을 통해 행위 주체성을 유지할 수 있다.

聴覚に着目したEEGチャンネル選択による効率的な音声強調

EEGを利用して複雑な多話者環境における目標話者の音声を抽出する手法を提案する。

Unified Multimodal Decoding of Brain Signals for Improved Understanding of Visual Concepts and Spatial Relationships

UMBRAE, a unified multimodal decoding method, aligns brain signals with image features to recover both semantic and spatial information, enabling a range of downstream tasks such as brain captioning, grounding, and retrieval.

Decoding Natural Images from Electroencephalography (EEG) Signals for Robust Object Recognition

A self-supervised framework that can effectively decode natural images from EEG signals, achieving state-of-the-art performance on large-scale zero-shot object recognition tasks.

ArEEG_Chars: A Dataset for Envisioned Arabic Speech Recognition using EEG Signals

The authors have created the first dataset for EEG signals of all Arabic characters, named ArEEG_Chars, and evaluated baseline deep learning models to classify Arabic characters from EEG signals automatically.

Towards a Gaze-Independent Brain-Computer Interface Using Code-Modulated Visual Evoked Potentials

This pilot study demonstrates the feasibility of decoding code-modulated visual evoked potentials (c-VEP) in a gaze-independent manner using covert spatial attention, providing the first steps towards a high-speed neuro-technological assistive device for individuals who may not have reliable control of their eye movements.

Psychometry: An Omnifit Model for Reconstructing Images from Diverse Human Brain Activity

Psychometry is an omnifit model that can efficiently capture both the inter-subject commonalities and individual specificities in fMRI data to reconstruct high-quality and realistic images.

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