Decoding Auditory Attention From EEG Data Using Cepstral Analysis

• Recent studies of selective auditory attention have demonstrated that neural responses recorded with electroencephalogram (EEG) can be decoded to classify the attended talker in everyday multitalker cocktail-party environments. This is generally referred to as the auditory attention decoding (AAD) and could lead to a breakthrough for the next-generation of hearing aids (HAs) to have the ability to be cognitively controlled. The aim of this paper is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary analysis revealed an average AAD accuracy of 96%. Moreover, we observed a significant increase in auditory attention classification accuracies with our approach over the use of traditional AAD methods (7% absolute increase). Overall, our exploratory study could open a new avenue for developing new AAD methods to further advance hearing technology. We recognize that additional research is needed to elucidate the full potential of cepstral analysis for AAD.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Oto-rhino-laryngologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Otorhinolaryngology (hsv//eng)

Keyword

auditory attention decoding

cepstral analysis

EEG

speech processing

stimulus reconstruction

auditory attention decoding; stimulus reconstruction; speech processing; EEG; cepstral analysis

Publication and Content Type

kon (subject category)

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