Taking NIRS-BCIs Outside the Lab: Towards Achieving Robustness Against Environment Noise

Abstract

Abstract-This paper reported initial findings on the effects of environmental noise and auditory distractions on the performance of mental state classification based on near-infrared spectroscopy (NIRS) signals recorded from the prefrontal cortex. Characterization of the performance losses due to environmental factors could provide useful information for the future development of NIRS-based brain-computer interfaces that can be taken beyond controlled laboratory settings and into everyday environments. Experiments with a hidden Markov model-based classifier showed that while significant performance could be attained in silent conditions, only chance levels of sensitivity and specificity were obtained in noisy environments. In order to achieve robustness against environment noise, two strategies were proposed and evaluated. First, physiological responses harnessed from the autonomic nervous system were used as complementary information to NIRS signals. More specifically, four physiological signals (electrodermal activity, skin temperature, blood volume pulse, and respiration effort) were collected in synchrony with the NIRS signals as the user sat at rest and/or performed music imagery tasks. Second, an acoustic monitoring technique was proposed and used to detect startle noise events, as both the prefrontal cortex and ANS are known to involuntarily respond to auditory startle stimuli. Experiments with eight participants showed that with a startle noise compensation strategy in place, performance comparable to that observed in silent conditions could be recovered with the hybrid ANS-NIRS system. Index Terms-Ambient noise, autonomic nervous system, hidden Markov models, music imagery, near-infrared spectroscopy.

Client-Centred Music Imagery Classification Based on Hidden Markov Models of Baseline Prefrontal Hemodynamic Response

“…All auditory and visual distractions were suppressed as much as possible by the earmuffs and blindfold. If BCI technologies are to be used in everyday settings, however, movement artifacts [10], auditory and visual distractions [42], and mind-wandering still have to be detected and accounted for (e.g., as in [19]). Additionally, experimental sessions lasted less than one hour.…”

“…As a consequence, by measuring the intensity of the reflected light, NIRS technologies can be used to examine regional cortical activity with depths of up to 2 cm [10]. Previous research has shown that NIRS can be used to assess hemodynamic responses in the motor cortex using activities such as motor imagery [3,7], as well as in the prefrontal cortex using higher cognitive tasks such as mental arithmetic [6,11,12], working memory [13,14], and emotion induction [15,16], both in silent (e.g., [17,18]) and noisy environments [19].…”

Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities

“…(2.6) during the measurement simulation. Although fNIRS systems are a lot less sensitive to movement than fMRI, this type of artefact can cause problems and methods for motion artefact removal are an active research area [16,47,48].…”

“…In ERS/ERD analysis, a baseline "reference" period of EEG data is recorded before the event and then compared to an "activity" period of EEG data, recorded during or following the event. ERD is known to occur in the frequency range (8-12 Hz) on movement onset and ERS is known to occur in theˇfrequency range (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) following movement offset.…”

Physiological noise cancellation in fNIRS using an adaptive filter based on mutual information

“…Prominent biosignals that are visible in fNIRS include respiration (0.2-0.4 Hz) and cardiac activity (blood pressure and cardiac pulse, up to 5.0 Hz). These biosignals respond to various tasks, such as isometric pinching [6] and music imagery [7], as part of the autonomic nervous system (ANS) response. The cortical hemodynamics respond at low frequencies (up to 0.2 Hz), so band-pass filters can remove faster signals [5] but do not remove in-band biosignal contributions.…”