![]() ![]() Therefore, the information transfer rate of such a BCI remains modest. However, this approach allows attention target to be determined only at the rate the deviants are presented, and this rate cannot be increased above 10 or 20% of all stimuli without diminishing the overall amplitude of the deviant responses. Selective attention then increases the amplitude of the response to a deviant compared to an unattended stimulus 16, 34, 35. Yet, near real-time performance has been demonstrated through advanced modelling 33.Īuditory streaming BCIs often employ oddball streams, comprising frequently-occurring stimuli (standard) and a rarely-occurring exception (deviant) 30. However, BCIs utilizing speech tracking often require long data spans (usually tens of seconds) to output one bit since the dynamics of continuous natural speech are complex and thus the responses less salient than those for isolated words or simple tone pips (see e.g., Ref. In comparison to speller-BCIs, BCIs based on either speech tracking or on detecting infrequent and unexpected changes in auditory streams could be designed such that their working-memory load is limited 21, 29, 30, 31. Furthermore, in patients with disorders of consciousness, using this type of a BCI may exceed the capacity of their working memory 25, 28, which could drastically drop the accuracy. However, these auditory speller-type BCI systems require extensive training that might be exhausting for a patient. Exploiting such attention modulation in a brain–computer interface has been probed in several studies 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, some of which have employed natural sounds as stimuli and yielded a useful-in-practice classification accuracy also when applied to patients that cannot communicate 26, 27. More recently, machine-learning methods have been applied to EEG/MEG data to study attention modulation of transient auditory evoked responses 9, auditory steady-state responses 10, 11 or responses to continuous speech 12, 13, 14, 15. Selectively attending to one stream manifests as changes in auditory evoked responses that can be measured non-invasively with electroencephalography (EEG) and magnetoencephalography (MEG) 4, 5, 6, 7, 8. Selective auditory attention enables filtering of relevant acoustic information from irrelevant and is often studied using dichotic listening 1, 2 where the listener is exposed to simultaneous but different auditory streams to each ear and is asked to follow one stream while suppressing the other, akin to the cocktail party problem 3. in an intuitive brain–computer interface. ![]() Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. The discriminating information was mostly available 200–400 ms after the stimulus onset. unattended words resulted in a mean accuracy of \(79\% \pm 2 \%\) ( N = 14) for both stimulus words. Sensor-level decoding of the responses to attended vs. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. The subjects were asked to attend to one speaker. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words “Yes” and “No” in an interleaved manner. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. However, such attention effects have typically been studied in unnatural conditions (e.g. ![]() Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. ![]() Selective auditory attention enables filtering of relevant acoustic information from irrelevant. ![]()
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