Development and Preliminary Evaluation of a Brain–Computer Interface for Word Recognition Using OpenBCI and Python
Abstract
We present an integrated approach for decoding brain activity associated with language processing using an OpenBCI brain–computer interface (BCI) coupled with Python-based signal acquisition and deep learning pipelines. In this study, subjects are presented with 50 words that they either read or internally visualize, while the BCI records their neural activity. We detail our data acquisition process using the BrainFlow API, describe our data serialization strategy, and outline our experimental protocol. Furthermore, we introduce a deep learning framework that employs convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) for temporal sequence modeling. Our preliminary results indicate that the integrated approach has strong potential for decoding cognitive states related to language processing.
