Auditory selective attention as a method for a brain computer interface

The object of this study was to design a Brain Computer Interface (BCI) based on auditory selective attention (ASA). ASA is a promising paradigm for a BCI, as focusing attention does not requite a lot of training, whereas the possibility of offering a large number of possible targets facilitates a high bit rate. In this study subjects focused attention on one tone out of two. The two tones were separated in space and pitch, and each tone was frequency tagged by means of amplitude modulation (AM). AM tones are known to evoke an auditory steady state response (ASSR) at the am frequency, and previous research has demonstrated that the power of this ASSR is increased by selective attention. To detect the direction of the subject’s attention, features were calculated that characterized the ASSR. Subsequently, a classifier was trained based on linear discriminant analysis. The best results were obtained with a feature that consisted of the real and imaginary parts of the Fourier transformed signal at the am-frequency. Electrodes above the auditory cortices yielded the best results. On perception data, single trial classification reached a classification rate of 80%. On attention data, the best classification rate was 68%. The current BCI achieved a bit rate of 3.78 bits/min, which is moderate compared to other BCI-systems. We will discuss several procedures for improvement.

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