BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916376902369280 |
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| author | Zhou, Jinzhao Duan, Yiqun Chang, Fred Do, Thomas Wang, Yu-Kai Lin, Chin-Teng |
| author_facet | Zhou, Jinzhao Duan, Yiqun Chang, Fred Do, Thomas Wang, Yu-Kai Lin, Chin-Teng |
| contents | The remarkable success of large language models (LLMs) across various multi-modality applications is well established. However, integrating large language models with humans, or brain dynamics, remains relatively unexplored. In this paper, we introduce BELT-2, a pioneering multi-task model designed to enhance both encoding and decoding performance from EEG signals. To bolster the quality of the EEG encoder, BELT-2 is the first work to innovatively 1) adopt byte-pair encoding (BPE)-level EEG-language alignment and 2) integrate multi-task training and decoding in the EEG domain. Inspired by the idea of \textbf{\textit{Bridging the Brain with GPT}}, we further connect the multi-task EEG encoder with LLMs by utilizing prefix-tuning on intermediary output from the EEG encoder. These innovative efforts make BELT-2 a pioneering breakthrough, making it the first work in the field capable of decoding coherent and readable sentences from non-invasive brain signals. Our experiments highlight significant advancements over prior techniques in both quantitative and qualitative measures, achieving a decoding performance with a BLEU-1 score of 52.2\% on the ZuCo dataset. Furthermore, BELT-2 shows a remarkable improvement ranging from 31\% to 162\% on other translation benchmarks. Codes can be accessed via the provided anonymous link~\footnote{https://anonymous.4open.science/r/BELT-2-0048}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00121 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding Zhou, Jinzhao Duan, Yiqun Chang, Fred Do, Thomas Wang, Yu-Kai Lin, Chin-Teng Signal Processing Artificial Intelligence Machine Learning Audio and Speech Processing The remarkable success of large language models (LLMs) across various multi-modality applications is well established. However, integrating large language models with humans, or brain dynamics, remains relatively unexplored. In this paper, we introduce BELT-2, a pioneering multi-task model designed to enhance both encoding and decoding performance from EEG signals. To bolster the quality of the EEG encoder, BELT-2 is the first work to innovatively 1) adopt byte-pair encoding (BPE)-level EEG-language alignment and 2) integrate multi-task training and decoding in the EEG domain. Inspired by the idea of \textbf{\textit{Bridging the Brain with GPT}}, we further connect the multi-task EEG encoder with LLMs by utilizing prefix-tuning on intermediary output from the EEG encoder. These innovative efforts make BELT-2 a pioneering breakthrough, making it the first work in the field capable of decoding coherent and readable sentences from non-invasive brain signals. Our experiments highlight significant advancements over prior techniques in both quantitative and qualitative measures, achieving a decoding performance with a BLEU-1 score of 52.2\% on the ZuCo dataset. Furthermore, BELT-2 shows a remarkable improvement ranging from 31\% to 162\% on other translation benchmarks. Codes can be accessed via the provided anonymous link~\footnote{https://anonymous.4open.science/r/BELT-2-0048}. |
| title | BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding |
| topic | Signal Processing Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.00121 |