Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.
Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.
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用于脑机通信接口的语言模型引导分类器适应。
DOI：
10.1109/smc53654.2022.9945561
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Mainsah,BoylaO
中科院分区：
文献类型：
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作者：
Chen,XinlinJ;Collins,LeslieM;Mainsah,BoylaO
脑机接口（BCI），如P300拼写器，可以为严重神经肌肉限制的人提供一种交流手段。BCI解释脑电图（EEG）信号，以便将有关用户意图的嵌入信息转换为可执行命令以控制外部设备。然而，EEG信号固有的噪声和非平稳性，对扩展BCI的使用提出了挑战。传统上，BCI分类器在离线校准会话中经由监督学习来训练;一旦训练，分类器就被部署用于在线使用并且不被更新。由于用户的EEG数据的统计随时间变化，静态分类器的性能可能随着扩展使用而下降。因此，期望自动地使分类器适应当前数据统计而不需要离线重新校准。在现有的半监督学习方法中，分类器在标记的EEG数据上训练，然后使用传入的未标记的EEG数据和分类器预测的标签进行更新。为了降低从不正确的预测中学习的风险，在重新训练自适应分类器时，施加阈值以从扩展训练集中排除具有低置信度标签预测的未标记数据。在这项工作中，我们提出了使用一个语言模型的拼写错误纠正和消歧，以提供信息的标签正确性在半监督学习。多会话P300拼写用户EEG数据的模拟结果表明，相对于传统的BCI校准和基于阈值的半监督学习，我们的语言引导的半监督方法显着提高了拼写准确性。
Brain-computer interfaces (BCIs), such as the P300 speller, can provide a means of communication for individuals with severe neuromuscular limitations. BCIs interpret electroencephalography (EEG) signals in order to translate embedded information about a user’s intent into executable commands to control external devices. However, EEG signals are inherently noisy and nonstationary, posing a challenge to extended BCI use. Conventionally, a BCI classifier is trained via supervised learning in an offline calibration session; once trained, the classifier is deployed for online use and is not updated. As the statistics of a user’s EEG data change over time, the performance of a static classifier may decline with extended use. It is therefore desirable to automatically adapt the classifier to current data statistics without requiring offline recalibration. In an existing semi-supervised learning approach, the classifier is trained on labeled EEG data and is then updated using incoming unlabeled EEG data and classifier-predicted labels. To reduce the risk of learning from incorrect predictions, a threshold is imposed to exclude unlabeled data with low-confidence label predictions from the expanded training set when retraining the adaptive classifier. In this work, we propose the use of a language model for spelling error correction and disambiguation to provide information about label correctness during semi-supervised learning. Results from simulations with multi-session P300 speller user EEG data demonstrate that our language-guided semi-supervised approach significantly improves spelling accuracy relative to conventional BCI calibration and threshold-based semi-supervised learning.