Word-specific properties affect classification performance in brain computer interfaces for decoding imagined speech from EEG

Abstract

Decoding imagined speech from brain signals has become one of the most significant fields for BCI applications. One of the current challenges that researchers face is an insufficient classification performance for real-world applications. In this study, we investigate for the first time the effect of word-specific properties known to modulate brain signals on classification performance. We chose 16 word prompts that vary in age of acquisition (AoA) and word frequency, two word-specific properties known to modulate speech processing, and investigated their classification performance for speech imagery (SI) trials compared to the idle state using a random forest classifier and 10-fold cross-validation. We found highly significant effects of AoA, word frequency and their interaction on classification performance. Our results yield evidence that the word frequency and AoA of word prompts used in SI paradigms significantly influence the classification accuracy in a BCI application when SI trials are compared to the idle state.