基于变换器和基于VAE的数据增强的EEG语音感知解码
Decoding EEG Speech Perception with Transformers and VAE-based Data Augmentation
AI总结：
本研究利用VAE和变换器模型提升EEG语音解码性能，通过数据增强和序列到序列架构改进语音感知任务。
摘要：
从非侵入性脑信号（如脑电图EEG）中解码语音，有潜力推动脑机接口（BCIs）的发展，应用于沉默通信和为有言语障碍者提供辅助技术。然而，基于EEG的语音解码面临重大挑战，如数据噪声、数据集有限以及在复杂任务如语音感知上的表现不佳。本研究试图通过变分自编码器（VAEs）对EEG数据进行增强以提高数据质量，并应用一种最先进的（SOTA）序列到序列深度学习架构，在EMG任务中取得成功，用于EEG语音解码。此外，我们还将此架构适应于单词分类任务。使用Brennan数据集，该数据集包含受试者听叙述语音的EEG记录，我们预处理数据并评估了分类和序列到序列模型在EEG到单词/句子任务中的表现。我们的实验表明，VAEs有潜力重建人工EEG数据以进行增强。同时，我们的序列到序列模型在生成句子方面比我们的分类模型更具前景，尽管两者仍然是具有挑战性的任务。这些发现为未来EEG语音感知解码的研究奠定了基础，可能扩展到语音生成任务，如沉默或想象的语音。
英文摘要：
Decoding speech from non-invasive brain signals, such as electroencephalography (EEG), has the potential to advance brain-computer interfaces (BCIs), with applications in silent communication and assistive technologies for individuals with speech impairments. However, EEG-based speech decoding faces major challenges, such as noisy data, limited datasets, and poor performance on complex tasks like speech perception. This study attempts to address these challenges by employing variational autoencoders (VAEs) for EEG data augmentation to improve data quality and applying a state-of-the-art (SOTA) sequence-to-sequence deep learning architecture, originally successful in electromyography (EMG) tasks, to EEG-based speech decoding. Additionally, we adapt this architecture for word classification tasks. Using the Brennan dataset, which contains EEG recordings of subjects listening to narrated speech, we preprocess the data and evaluate both classification and sequence-to-sequence models for EEG-to-words/sentences tasks. Our experiments show that VAEs have the potential to reconstruct artificial EEG data for augmentation. Meanwhile, our sequence-to-sequence model achieves more promising performance in generating sentences compared to our classification model, though both remain challenging tasks. These findings lay the groundwork for future research on EEG speech perception decoding, with possible extensions to speech production tasks such as silent or imagined speech.
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