优化用于解码自然语音的fMRI数据采集：针对有限参与者的情况
Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants
AI总结：
本研究探讨了在有限参与者下从fMRI解码自然语音的最优策略，发现多受试者训练及不同刺激对解码准确率影响甚微，指出深度表型分析或更大队列可能是提升解码效果的关键。
摘要：
我们研究从有限参与者采集的fMRI数据中解码感知到的自然语音的最优策略。利用Lebel等人（2023）的8名参与者数据集，我们首先证明了训练深度神经网络从fMRI活动中预测LLM衍生的文本表示的有效性。然后，在此数据规模下，我们观察到与单受试者方法相比，多受试者训练并未提高解码准确率。此外，在受试者间使用相似或不同刺激进行训练对解码准确率的影响可忽略不计。最后，我们发现解码器对句法特征的建模优于语义特征，包含复杂句法或丰富语义内容的故事更具解码挑战性。尽管我们的结果证明了每位参与者拥有大量数据（深度表型分析）的益处，但它们表明利用多受试者进行自然语音解码可能需要更深度的表型分析或更大规模的队列。
英文摘要：
We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8 participants, we first demonstrate the effectiveness of training deep neural networks to predict LLM-derived text representations from fMRI activity. Then, in this data regime, we observe that multi-subject training does not improve decoding accuracy compared to single-subject approach. Furthermore, training on similar or different stimuli across subjects has a negligible effect on decoding accuracy. Finally, we find that our decoders better model syntactic than semantic features, and that stories containing sentences with complex syntax or rich semantic content are more challenging to decode. While our results demonstrate the benefits of having extensive data per participant (deep phenotyping), they suggest that leveraging multi-subject for natural speech decoding likely requires deeper phenotyping or a substantially larger cohort.
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