Purpose. Work performance is closely related to one's attention level. In this study, a brain-computer interface (BCI) device suitable for office usage was chosen to quantify the individual's attention levels. Methods. A BCI system was adopted to interface brainwave signals to a coffee maker via three ascending levels of laser detectors. The preliminary test with this prototype was to characterize the attention level through the collected coffee amount. Here, the preliminary testing was comparing the correlation between the attention level and the participants' cumulative grade point average (CGPA) and scores from the 21-item depression, anxiety, and stress scale (DASS-21) and the attentional control scale (ACS) using ordinal regression. It was assumed that a greater CGPA would generate a greater attention level. Result. The generated coffee amount from the BCI system had a significant positive correlation with the CGPA (p = 0.004), mild depression (p = 0.019) and mild and extremely severe anxiety (p = 0.044 and p = 0.019, respectively) and a negative correlation with the ACS score (p = 0.042). Conclusion. This simple and cost-effective prototype has the potential to enable everyone to know their immediate attention level and predict the possible correlation to their mental state.
Neurofeedback (NFB) allows subjects to learn how to volitionally influence the neuronal activation in the brain by employing real-time neural activity as feedback. NFB has already been performed with electroencephalography (EEG) since the 1970s. Functional MRI (fMRI), offering a higher spatial resolution, has further increased the spatial specificity. In this paper, we briefly outline the general principles behind NFB, the implementation of fMRI-NFB studies, the feasibility of fMRI-NFB, and the application of NFB as a supplementary therapy tool.
To establish the effectiveness of EEG biofeedback using beta training as a relaxation technique and ultimately reducing anxiety levels of patients with confirmed unstable angina or myocardial infarction.
Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings.
The aim of this paper is to develop a preliminary neurofeedback system to improve the mood of the subjects using audio signals by enhancing their alpha brainwaves. Assessment of the effect of music on the human subjects is performed using three methods; subjective assessment of mood with the help of a questionnaire, the effect on brain by analysing EEG signals, and the effect on body by physiological assessment. In this study, two experiments have been designed. The first experiment was to determine the short-term effect of music on soothing human subjects, whereas the second experiment was to determine its long-term effect. Two types of music were used in the first experiment, the favourite music selected by the participants and a relaxing music with alpha wave binaural beats. The research findings showed that the relaxing music has a better soothing effect on the participants psychologically and physiologically. However, the one-way analysis of variance (ANOVA) results showed that the short-term soothing effect of both favourite music and relaxing music was not significant in changing the mean alpha absolute power and mean physiological measures (blood pressure and heart rate) at the significance level of 0.05. The second experiment was somewhat similar to an alpha neurofeedback training whereby the participants trained their brains to produce more alpha brainwaves by listening to the relaxing music with alpha wave binaural beats for a duration of 30 min daily. The results showed that the relaxing music has a long-term psychological and physiological effect on soothing the participants, as can be observed from the increase in alpha power and decrease in physiological measures after each session of training. The training was found to be effective in increasing the alpha power significantly [F(2,12) = 11.5458 and p = 0.0016], but no significant reduction in physiological measures was observed at the significance level of 0.05.
Emotion is a crucial aspect of human health, and emotion recognition systems serve important roles in the development of neurofeedback applications. Most of the emotion recognition methods proposed in previous research take predefined EEG features as input to the classification algorithms. This paper investigates the less studied method of using plain EEG signals as the classifier input, with the residual networks (ResNet) as the classifier of interest. ResNet having excelled in the automated hierarchical feature extraction in raw data domains with vast number of samples (e.g., image processing) is potentially promising in the future as the amount of publicly available EEG databases has been increasing. Architecture of the original ResNet designed for image processing is restructured for optimal performance on EEG signals. The arrangement of convolutional kernel dimension is demonstrated to largely affect the model's performance on EEG signal processing. The study is conducted on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED), with our proposed ResNet18 architecture achieving 93.42% accuracy on the 3-class emotion classification, compared to the original ResNet18 at 87.06% accuracy. Our proposed ResNet18 architecture has also achieved a model parameter reduction of 52.22% from the original ResNet18. We have also compared the importance of different subsets of EEG channels from a total of 62 channels for emotion recognition. The channels placed near the anterior pole of the temporal lobes appeared to be most emotionally relevant. This agrees with the location of emotion-processing brain structures like the insular cortex and amygdala.
This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.
Competitive football players who undergo strenuous training and frequent competitions are more vulnerable to psychological disorders. Probiotics are capable of reducing these psychological disorders. The present study aimed to determine the effect of daily probiotics supplementation on anxiety induced physiological parameters among competitive football players. The randomized, double-blinded, placebo-controlled trial was conducted on 20 male footballers who received either probiotics (Lactobacillus Casei Shirota strain 3 × 1010 colony forming units (CFU) or a placebo drink over eight weeks. Portable biofeedback devices were used to measure the electroencephalography, heart rate, and electrodermal responses along with cognitive tests at the baseline, week 4, and week 8. Data were statistically analyzed using mixed factorial ANOVA and results revealed that there is no significant difference between the probiotic and placebo groups for heart rate (61.90 bpm ± 5.84 vs. 67.67 bpm ± 8.42, p = 0.09) and electrodermal responses (0.27 µS ± 0.19 vs. 0.41 µS ± 0.12, p = 0.07) after eight weeks. Similarly, brain waves showed no significant changes during the study period except for the theta wave and delta wave at week 4 (p < 0.05). The cognitive test reaction time (digit vigilance test) showed significant improvement in the probiotic group compared to the placebo (p < 0.05). In conclusion, these findings suggest that daily probiotics supplementation may have the potential to modulate the brain waves namely, theta (relaxation) and delta (attention) for better training, brain function, and psychological improvement to exercise. Further research is needed to elucidate the mechanism of current findings.