Improved decoding of attentional selection in a cocktail party environment with EEG via automatic selection of relevant independent components

Recently it has been shown to be possible to ascertain which speaker a subject is attending to in a cocktail party environment from single-trial (~60s) electroencephalography (EEG) data. The attentional selection of most of subjects could be decoded with a very high accuracy (>90%). However, the performance of many subjects fell below what would be required for a potential brain computer interface (BCI). One potential reason for this is that activity related to the stimuli may have a lower signal-to-noise ratio on the scalp for some subjects than others. Independent component analysis (ICA) is a commonly used method for denoising EEG data. However, its effective use often requires the subjective choice of the experimenter to determine which independent components (ICs) to retain and which to reject. Algorithms do exist to automatically determine the reliability of ICs, however they provide no information as to their relevance for the task at hand. Here we introduce a novel method for automatically selecting ICs which are relevant for decoding attentional selection. In doing so, we show a significant increase in classification accuracy at all test data durations from 60s to 10s. These findings have implications for the future development of naturalistic and user-friendly BCIs, as well as for smart hearing aids.