Objective: Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. The goal of this study is to evaluate the BCI performance of a robust speech decoding system that translates neural signals evoked by speech to a textual output. While previous studies have approached this problem by using neural signals to choose from a limited set of possible words, we employ a more general model that can type any word from a large corpus of English text. Approach: In this study, we create an end-to-end BCI that translates neural signals associated with overt speech into text output. Our decoding system first isolates frequency bands in the input depth-electrode signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that then feeds into a Long Short-Term Memory (LSTM) model which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities by incorporating prior knowledge of the English language to output text corresponding to the decoded word. The generalizability of our decoder is driven by the lack of a vocabulary constraint on this output word. Main result: This method was evaluated using a dataset of 6 neurosurgical patients implanted with intra-cranial depth electrodes to identify seizure foci for potential surgical treatment of epilepsy. We averaged 32% word accuracy and on the phoneme-level obtained 46% precision, 51% recall and 73.32% average phoneme error rate while also achieving significant increases in speed when compared to several other BCI approaches. Significance: Our study employs a more general neural signal-to-text model which could facilitate communication by patients in everyday environments.

目的：脑机接口 (BCI) 可以帮助因神经退行性疾病而导致交流能力下降的患者通过直接神经处理产生文本或语音。然而，由于现有接口的速度、准确性和通用性的限制，它们的实际实现已被证明是困难的。本研究的目的是评估强大的语音解码系统的 BCI 性能，该系统将语音诱发的神经信号转换为文本输出。虽然之前的研究已经通过使用神经信号从一组有限的可能单词中进行选择来解决这个问题，但我们采用了一个更通用的模型，可以从大量英文文本中输入任何单词。方法：在这项研究中，我们创建了一个端到端的 BCI，它将与公开语音相关的神经信号转换为文本输出。我们的解码系统首先隔离输入深度电极信号中的频带，该信号封装了有关各种音位类别产生的差异信息。这些频带形成了一个特征集，然后输入到一个长短期记忆 (LSTM) 模型中，该模型在每个时间点辨别受试者发出的所有音素的概率分布。最后，粒子滤波算法通过结合英语语言的先验知识来在时间上平滑这些概率，以输出对应于解码单词的文本。我们的解码器的泛化性是由于这个输出词缺乏词汇限制。主要结果：使用植入颅内深度电极的 6 名神经外科患者的数据集对该方法进行评估，以识别癫痫病灶以进行潜在的癫痫手术治疗。我们平均 32% 的单词准确率，在音素级别上获得了 46% 的准确率、51% 的召回率和 73.32% 的平均音素错误率，同时与其他几种 BCI 方法相比，速度也显着提高。意义：我们的研究采用了更通用的神经信号到文本模型，可以促进患者在日常环境中的交流。