Brain-computer interfaces have promising medical and scientific applications
for aiding speech and studying the brain. In this work, we propose an
information-based evaluation metric for brain-to-text decoders. Using this
metric, we examine two methods to augment existing state-of-the-art continuous
text decoders. We show that these methods, in concert, can improve brain
decoding performance by upwards of 40% when compared to a baseline model. We
further examine the informatic properties of brain-to-text decoders and show
empirically that they have Zipfian power law dynamics. Finally, we provide an
estimate for the idealized performance of an fMRI-based text decoder. We
compare this idealized model to our current model, and use our
information-based metric to quantify the main sources of decoding error. We
conclude that a practical brain-to-text decoder is likely possible given
further algorithmic improvements.

通过使用基于信息的评估指标，本研究提出了两种方法来改进现有先进的连续文本解码器，展示了这些方法的相互配合可将脑解码性能提高 40% 以上，并通过实证研究证明脑到文本解码具有 Zipfian 幂律动力学特性，最终通过信息度量方法量化解码误差的主要来源并估算了基于 fMRI 的文本解码器的理想性能与当前模型的差距，得出进一步改进算法可以实现实用脑到文本解码器的结论。