Effects of Background Music on Objective and Subjective Performance Measures in an Auditory BCI

Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs

As a widely used brain–computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants’ mental fatigue. The study’s results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants’ mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users’ mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.

Improving Cross-State and Cross-Subject Visual ERP-based BCI with Temporal Modeling and Adversarial Training

Brain-computer interface (BCI) is a useful device for people without relying on peripheral nerves and muscles. However, the performance of the event-related potential (ERP)-based BCI declines when applying it to real environments, especially in cross-state and cross-subject conditions. Here we employ temporal modeling and adversarial training to improve the visual ERP-based BCI under different mental workload states and to alleviate the problems above. The rationality of our method is that the ERP-based BCI is based on electroencephalography (EEG) signals recorded from the scalp's surface, continuously changing with time and somewhat stochastic. In this paper, we propose a hierarchical recurrent network to encode all ERP signals in each repetition at the same time and model them with a temporal manner to predict which visual event elicited an ERP. The hierarchical architecture is a simple yet effective method for organizing recurrent layers in a deep structure to model long sequence signals. Taking a cue from recent advances in adversarial training, we further applied dynamic adversarial perturbations to create adversarial examples to enhance the model performance. We conduct our experiments on one published visual ERP-based BCI task with 15 subjects and 3 different auditory workload states. The results indicate that our hierarchical method can effectively model the long sequence EEG raw data, outperform the baselines on most conditions, including cross-state and cross-subject conditions. Finally, we show how deep learning-based methods with limited EEG data can improve ERP-based BCI with adversarial training. Our code will be released at https://github.com/aispeech-lab/VisBCI.

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.

Speech stream segregation to control an ERP-based auditory BCI

OBJECTIVE

The use of natural sounds in auditory Brain-Computer Interfaces (BCI) has been shown to improve classification results and usability. Some auditory BCIs are based on stream segregation, in which the subjects must attend one audio stream and ignore the other(s); these streams include some kind of stimuli to be detected. In this work we focus on Event-Related Potentials (ERP) and study whether providing intelligible content to each audio stream could help the users to better concentrate on the desired stream and so to better attend the target stimuli and to ignore the non-target ones.

APPROACH

In addition to a control condition, two experimental conditions, based on the selective attention and the cocktail party effect, were tested using two simultaneous and spatialized audio streams: i) the condition A2 consisted of an overlap of auditory stimuli (single syllables) on a background consisting of natural speech for each stream, ii) in condition A3, brief alterations of the natural flow of each speech were used as stimuli.

MAIN RESULTS

The two experimental proposals improved the results of the control condition (single words as stimuli without a speech background) both in a cross validation analysis of the calibration part and in the online test. The analysis of the ERP responses also presented better discriminability for the two proposals in comparison to the control condition. The results of subjective questionnaires support the better usability of the first experimental condition.

SIGNIFICANCE

The use of natural speech as background improves the stream segregation in an ERP-based auditory BCI (with significant results in the performance metrics, the ERP waveforms, and in the preference parameter in subjective questionnaires). Future work in the field of ERP-based stream segregation should study the use of natural speech in combination with easily perceived but not distracting stimuli.

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

Studies so far have analyzed the effect of distractor stimuli in different types of brain-computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.

The Study of Influence of Sound on Visual ERP-Based Brain Computer Interface

The performance of the event-related potential (ERP)-based brain–computer interface (BCI) declines when applying it into the real environment, which limits the generality of the BCI. The sound is a common noise in daily life, and whether it has influence on this decline is unknown. This study designs a visual-auditory BCI task that requires the subject to focus on the visual interface to output commands and simultaneously count number according to an auditory story. The story is played at three speeds to cause different workloads. Data collected under the same or different workloads are used to train and test classifiers. The results show that when the speed of playing the story increases, the amplitudes of P300 and N200 potentials decrease by 0.86 μV (p = 0.0239) and 0.69 μV (p = 0.0158) in occipital-parietal area, leading to a 5.95% decline (p = 0.0101) of accuracy and 9.53 bits/min decline (p = 0.0416) of information transfer rate. The classifier that is trained by the high workload data achieves higher accuracy than the one trained by the low workload if using the high workload data to test the performance. The result indicates that the sound could affect the visual ERP-BCI by increasing the workload. The large similarity of the training data and testing data is as important as the amplitudes of the ERP on obtaining high performance, which gives us an insight on how make to the ERP-BCI generalized.

Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials

Recent research has demonstrated how brain-computer interfaces (BCI) based on auditory stimuli can be used for communication and rehabilitation. In these applications, users are commonly instructed to avoid eye movements while keeping their eyes open. This secondary task can lead to exhaustion and subjects may not succeed in suppressing eye movements. In this work, we investigate the option to use a BCI with eyes-closed. Twelve healthy subjects participated in a single electroencephalography (EEG) session where they were listening to a rapid stream of bisyllabic words while alternatively having their eyes open or closed. In addition, we assessed usability aspects for the two conditions with a questionnaire. Our analysis shows that eyes-closed does not reduce the number of eye artifacts and that event-related potential (ERP) responses and classification accuracies are comparable between both conditions. Importantly, we found that subjects expressed a significant general preference toward the eyes-closed condition and were also less tensed in that condition. Furthermore, switching between eyes-closed and eyes-open and vice versa is possible without a severe drop in classification accuracy. These findings suggest that eyes-closed should be considered as a viable alternative in auditory BCIs that might be especially useful for subjects with limited control over their eye movements.

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

There have been few reports that investigated the effects of the degree and pattern of a spectral smearing of stimuli due to deteriorated hearing ability on the performance of auditory brain-computer interface (BCI) systems. In this study, we assumed that such spectral smearing of stimuli may affect the performance of an auditory steady-state response (ASSR)-based BCI system and performed subjective experiments using 10 normal-hearing subjects to verify this assumption. We constructed smearing-reflected stimuli using an 8-channel vocoder with moderate and severe hearing loss setups and, using these stimuli, performed subjective concentration tests with three symmetric and six asymmetric smearing patterns while recording electroencephalogram signals. Then, 56 ratio features were calculated from the recorded signals, and the accuracies of the BCI selections were calculated and compared. Experimental results demonstrated that (1) applying smearing-reflected stimuli decreases the performance of an ASSR-based auditory BCI system, and (2) such negative effects can be reduced by adjusting the feature settings of the BCI algorithm on the basis of results acquired a posteriori. These results imply that by fine-tuning the feature settings of the BCI algorithm according to the degree and pattern of hearing ability deterioration of the recipient, the clinical benefits of a BCI system can be improved.

Usage of drip drops as stimuli in an auditory P300 BCI paradigm

Recently, many auditory BCIs are using beeps as auditory stimuli, while beeps sound unnatural and unpleasant for some people. It is proved that natural sounds make people feel comfortable, decrease fatigue, and improve the performance of auditory BCI systems. Drip drop is a kind of natural sounds that makes humans feel relaxed and comfortable. In this work, three kinds of drip drops were used as stimuli in an auditory-based BCI system to improve the user-friendness of the system. This study explored whether drip drops could be used as stimuli in the auditory BCI system. The auditory BCI paradigm with drip-drop stimuli, which was called the drip-drop paradigm (DP), was compared with the auditory paradigm with beep stimuli, also known as the beep paradigm (BP), in items of event-related potential amplitudes, online accuracies and scores on the likability and difficulty to demonstrate the advantages of DP. DP obtained significantly higher online accuracy and information transfer rate than the BP (p < 0.05, Wilcoxon signed test; p < 0.05, Wilcoxon signed test). Besides, DP obtained higher scores on the likability with no significant difference on the difficulty (p < 0.05, Wilcoxon signed test). The results showed that the drip drops were reliable acoustic materials as stimuli in an auditory BCI system.

An improved P300 pattern in BCI to catch user's attention

OBJECTIVE

Brain-computer interfaces (BCIs) can help patients who have lost control over most muscles but are still conscious and able to communicate or interact with the environment. One of the most popular types of BCI is the P300-based BCI. With this BCI, users are asked to count the number of appearances of target stimuli in an experiment. To date, the majority of visual P300-based BCI systems developed have used the same character or picture as the target for every stimulus presentation, which can bore users. Consequently, users attention may decrease or be negatively affected by adjacent stimuli.

APPROACH

In this study, a new stimulus is presented to increase user concentration. Honeycomb-shaped figures with 1-3 red dots were used as stimuli. The number and the positions of the red dots in the honeycomb-shaped figure were randomly changed during BCI control. The user was asked to count the number of the dots presented in each flash instead of the number of times they flashed. To assess the performance of this new stimulus, another honeycomb-shaped stimulus, without red dots, was used as a control condition.

MAIN RESULTS

The results showed that the honeycomb-shaped stimuli with red dots obtained significantly higher classification accuracies and information transfer rates (p  <  0.05) compared to the honeycomb-shaped stimulus without red dots.

SIGNIFICANCE

The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.