Music and natural sounds in an auditory steady-state response based brain–computer interface to increase user acceptance

Heo, Jeongyun; Baek, Hyun Jae; Hong, Seunghyeok; Chang, Moon Ho; Lee, Jeong Su; Park, Kwang Suk

doi:10.1016/j.compbiomed.2017.03.011

Cited by 30 publications

(28 citation statements)

References 34 publications

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“…Sugi and colleagues (2018) similarly employed spatially distinct sound sources (six in their case) and optimized the stimulus onset asynchrony for maximal information transfer rate; the optimal SOA was found to be 400–500 ms, which yielded over 85% accuracy when classifying the target sound source vs. all others. Heo and colleagues (2017) utilized piano and violin music, sounds of nature as well as pure tones which were all amplitude modulated at 38 and 42 Hz to elicit auditory steady-state responses. LDA classification of the EEG responses to sounds of nature yielded the highest accuracy (83%), and the authors argue that this due to the acceptance, or pleasantness, of these stimuli compared to the other stimuli in that study.…”

Section: Discussionmentioning

confidence: 99%

Target of selective auditory attention can be robustly followed with MEG

Kurmanavičiūtė

Rantala

Jas

et al. 2019

Preprint

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Selective auditory attention enables filtering relevant from irrelevant acoustic information. Specific auditory responses, measurable by electro-and magnetoencephalography (EEG/MEG), are known to be modulated by attention to the evoking stimuli. However, these attention effects are typically demonstrated in averaged responses and their robustness in single trials is not studied extensively.We applied decoding algorithms to MEG to investigate how well the target of auditory attention could be determined from single responses and which spatial and temporal aspects of the responses carry most of the information regarding the target of attention. To this end, we recorded brain responses of 15 healthy subjects with MEG when they selectively attended to one of the simultaneously presented auditory streams of words "Yes" and "No". A support vector machine was trained on the MEG data both at the sensor and source level to predict at every trial which stream was attended.Sensor-level decoding of the attended stream using the entire 2-s epoch resulted in a mean accuracy of 93% ± 1% (range 83-99% across subjects). Time-resolved decoding revealed that the highest accuracies were obtained 200-350 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the cortical sources most informative of the attended stream were located in the primary auditory cortex, especially in the right hemisphere.Our result corroborates attentional modulation of auditory evoked responses also to naturalistic stimuli. The achieved high decoding accuracy could enable the use of our experimental paradigm and classification method in a brain-computer interface.

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Section: Discussionmentioning

confidence: 99%

Target of selective auditory attention can be robustly followed with MEG

Kurmanavičiūtė

Rantala

Jas

et al. 2019

Preprint

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“…In the natural sound session, the sounds of water streaming and cicadas singing are normally used. 59 Functional electric stimulation (FES), neuromuscular electrical stimulation (NMES), biofeedback, bioness, virtual reality, robotics, neuroprosthetics, robotic-assisted gait training (RAGT), tablet applications, as technological achievements, have recently become frequently used adjunctive treatment in inpatient rehabilitation. [60][61][62] "Saebo package" is also very popular choice for rehabilitation.…”

Section: Therapeutic Approachesmentioning

confidence: 99%

Therapeutic approaches in locked-in syndrome

Papadopoulou

Dionyssiotis

Krikonis³

et al. 2019

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Locked-in syndrome (LIS) is a neuropsychological condition, in which patients present with quadriplegia, lower cranial nerve paralysis, and mutism. Diagnosis of LIS is difficult because of the similarities with other related syndromes, but it is of crucial importance to establish precise and early diagnosis in order to make the appropriate decisions according to the intervention and treatment planning. Access to a multidisciplinary, specialized team provides opportunity for continued improvement. Individualized treatment improves long-term management. Assistive technology and advanced communication aids may help people with disabilities to regain more independence and take part in everyday life. Technological achievements, such as brain-computer interfaces (BCIs) may potentially be of significant practical value to patients with LIS. Advancements in medical care, rehabilitation, and communication technology have focused on leading LIS patients to live meaningful lives in the society with the involvement of their families.

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“…Moreover, daily usage of tactile BCI is hard because most people do not have tactile stimulators at home (Kaufmann et al, 2013). Thus, there has been an increasing interest towards auditory BCI (aBCI), which mainly uses auditory selective attention (Hill et al, 2004;Kanoh et al, 2008;Nijboer et al, 2008;Furdea et al, 2009;Klobassa et al, 2009;Halder et al, 2010;Schreuder et al, 2010;Higashi et al, 2011;Höhne et al, 2011;Kim et al, 2011;Schreuder et al, 2011;Höhne et al, 2012;Kim et al, 2012;Lopez-Gordo et al, 2012b;Lopez-Gordo et al, 2012a;Käthner et al, 2013;Nakamura et al, 2013;Simon et al, 2014;Kleih et al, 2015;Halder et al, 2016;Zhou et al, 2016;Heo et al, 2017;Kaongoen and Jo, 2017;Jalilpour and Sardouie, 2018;Ogino et al, 2019) or auditory imagery (González et al, 2019) to influence event-related potentials (ERPs) and/or auditory steady-state responses (ASSRs). ASSR is chiefly evoked by listening to amplitude-modulated (AM) tones, and its spectrum has peaks at message frequency (fm) (Picton et al, 2003;Lopez et al, 2009;Tanaka et al, 2013;Tanaka et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In aBCI, lengthening the processing window enhances the classification accuracy, whereas it reduces the speed (Lopez-Gordo et al, 2012a). Also, when users eagerly listen to their interesting stimuli, the classification accuracy (Höhne et al, 2012;Treder et al, 2014;Zhou et al, 2016;Heo et al, 2017) and response amplitude (Kleih et al, 2010) rise. Moreover, rhythmic stimulation modulates the intrinsic neural oscillatory characteristics (Treder et al, 2014;Herrmann et al, 2016) and facilitates keeping focus (Sato et al, 2019).…”

Section: Introductionmentioning

confidence: 99%