An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps

Abstract

Visual brain-computer interfaces (BCIs) are not suitable for people who cannot reliably maintain their eye gaze. Considering that this group usually maintains audition, an auditory based BCI may be a good choice for them. In this paper, we explore two auditory patterns: (1) a pattern utilizing symmetrical spatial cues with multiple frequency beeps [called the high low medium (HLM) pattern], and (2) a pattern utilizing non-symmetrical spatial cues with six tones derived from the diatonic scale [called the diatonic scale (DS) pattern]. These two patterns are compared to each other in terms of accuracy to determine which auditory pattern is better. The HLM pattern uses three different frequency beeps and has a symmetrical spatial distribution. The DS pattern uses six spoken stimuli, which are six notes solmizated as "do", "re", "mi", "fa", "sol" and "la", and derived from the diatonic scale. These six sounds are distributed to six, spatially distributed, speakers. Thus, we compare a BCI paradigm using beeps with another BCI paradigm using tones on the diatonic scale, when the stimuli are spatially distributed. Although no significant differences are found between the ERPs, the HLM pattern performs better than the DS pattern: the online accuracy achieved with the HLM pattern is significantly higher than that achieved with the DS pattern (p = 0.0028).

Effect of Distracting Background Speech in an Auditory Brain–Computer Interface

“…Four different tones were used as stimuli: a beep at 200 Hz in the left earphone, a beep at 1000 Hz in the left earphone, a beep at 200 Hz in the right earphone, and a beep at 1000 Hz in the right earphone. These frequencies (tones) have been previously used elsewhere [ 33 ] in an auditory BCI. In all conditions, users had to attend to a designated target stimulus and ignore the other stimuli (primary task).…”

“…In order to proceed with the statistical analysis for P300 and to study how it may be affected under different conditions, a topographical analysis was carried out. The specific time intervals for this component were chosen based on the specific results of previous works on an auditory ERP-BCI (e.g., Huang et al [ 33 ], Hübner et al [ 34 ] and Onishi et al [ 37 ]). The program used for the analysis of the EEG signal was EEGLAB (v13.6.5b) [ 38 ].…”

BCI-Based Rehabilitation on the Stroke in Sequela Stage

“…In addition, motor imagery- (MI-) based BCI, one of the widely used BCI systems, could also be used in stroke rehabilitation to translate brain signals into intended movements [ 7 ]. Different from other paradigms such as event-related potentials (ERP) [ 8 , 9 ] and steady-state visual evoked potentials (SSVEP) [ 10 ], MI by the BCI user elicits an event-related (de)synchronization (ERD/S) in the electroencephalogram (EEG), which represents the result of conscious access to the content of the intention of a movement [ 11 – 13 ]. Therefore, the ERD/S features may be used to detect motor intention in stroke patients.…”

Effects of spectral smearing of stimuli on the performance of auditory steady-state response-based brain–computer interface

“…Commonly, the positions of measurement/reference/ground electrodes and the performances of the utilized device and detection algorithm affect the system performance (Lotte et al 2007). Additionally, in SSVEP BCI, the flickering frequency, intensity, color, pattern, background, and size of visual stimuli and the arrangement of light sources (e.g., light emitting diodes or patterns on a monitor) can affect the system performance (Allison et al 2008;Jukiewicz and Cysewska-Sobusiak 2016;Duszyk et al 2014); in ASSR BCI, the frequency, intensity, and pattern of auditory stimuli and the arrangement of audio sources (e.g., speakers, earphones, or vibrators) can affect the system performance (Matsumoto et al 2012;Nakamura et al 2013); and in P300 BCI, the background noise, presentation paradigm, stimuli types (e.g., sound or visual), and user's attention can affect the system performance (Zhou et al 2016;Huang et al 2016;Jin et al 2015Jin et al , 2017. However, considering that the values of features that are the basis of BCI selection-e.g., the amplitudes of SSVEP, ASSR, or P300 signals-are calculated from the brain responses that are induced by certain intracranial processes that have not yet been clearly identified, it can be assumed that the pathophysiological statuses of distal stimulus-sensing organs, such as eyes or ears, and those of the sensing and processing parts of the brainstem and brain (Ortner et al 2011), as well as psychological factors, such as concentration on the stimulus and comfort in concentration (Voicikas et al 2016), may also affect the values of BCIrelated features and, as a result, affect the performance of the BCI system.…”