Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text
원문 초록
The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It's important to outline this area's recent developments and future research directions. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. Firstly, we talk about how EEG-to-text technology has grown and what problems we still face. Secondly, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective Brain-Computer Interface (BCI) technology for a broader user base.
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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
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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
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