Attentional Indicators and Quantitative EEG Alterations through Respiratory Pattern Manipulation
Poster No:
2460
Submission Type:
Abstract Submission
Authors:
Dogeun Park1, Young-Gi Ju1, Dong-Ok Won1,2
Institutions:
1Dept. of Artificial Intelligence Convergence, Hallym University, Chuncheon, Kangwon, Korea, Republic of, 2College of Medcine, Hallym University, Chuncheon, Kangwon, Korea, Republic of
First Author:
Dogeun Park
Dept. of Artificial Intelligence Convergence, Hallym University
Chuncheon, Kangwon, Korea, Republic of
Dept. of Artificial Intelligence Convergence, Hallym University
Chuncheon, Kangwon, Korea, Republic of
Co-Author(s):
Young-Gi Ju
Dept. of Artificial Intelligence Convergence, Hallym University
Chuncheon, Kangwon, Korea, Republic of
Dept. of Artificial Intelligence Convergence, Hallym University
Chuncheon, Kangwon, Korea, Republic of
Dong-Ok Won
Dept. of Artificial Intelligence Convergence, Hallym University|College of Medcine, Hallym University
Chuncheon, Kangwon, Korea, Republic of|Chuncheon, Kangwon, Korea, Republic of
Dept. of Artificial Intelligence Convergence, Hallym University|College of Medcine, Hallym University
Chuncheon, Kangwon, Korea, Republic of|Chuncheon, Kangwon, Korea, Republic of
Introduction:
Attention enhancement is a valuable factor for attention-related disorders such as attention deficit/hyperactivity disorder (ADHD), and well-functioning in the work environment in modern society. Numerous studies have shown that theta/beta ratio (TBR) is elevated in ADHD, and these have provided evidence that TBR might be an electrophysiological marker for attentional control [1]. ERP-based BCI is a representative attention-based paradigm that could induce visual and cognitive fatigue [2]. One previous study showed improved performance through six minutes of meditative mindfulness induction. A study comparing four different respiratory patterns, including meditation, demonstrated that five minutes of physiological sighing were more effective than mindfulness meditation in terms of reducing physiological arousal, and another preliminary study showed that physiological sighing enhanced character recognition performance in ERP-based BCI. Therefore, we assumed that physiological sighing could improve attention through reducing stress and investigated the theta/beta ratio, which is one of the quantitative indicators of attentional control, to provide additional explanatory power for ERP-based BCI performance improvement.
Methods:
As shown in Figure 1, physiological sighing consists of double inhalations and single extended exhalation. A second inhalation is performed to expand the alveoli. In this preliminary study, a total of five subjects participated. Each subject was requested to experience two sessions, and the experiment consists of two sessions: 1) Session A; 2) Session B, and two conditions: 1) Normal Condition; 2) Proposed Condition. An average of 15 minutes of break time was provided in the middle of the two sessions. We used MATLAB with the BBCI toolbox (http://bbci.de/toolbox) for pre-processing, and regularized linear discriminant analysis with shrinkage was applied with Python (version 3.7.11). To ascertain the feasibility of the hypothesis, Welch's method and the composite Simpson's rule were applied to estimate band power and the theta/beta ratio.
Results:
This study demonstrated a lower mean TBR in the proposed conditions using physiological sighing (Figure 2). In addition, these results showed a lower mean TBR in the proposed respiratory patterns in both Session A and Session B. In individual analysis, we showed low TBR under all subjects' conditions except for the second session of subject 4. Interestingly, subject 4 had dry eye syndrome and showed low performance in BCI. This indicated that the BCI performance and the results of TBR are matched.
Conclusions:
We examined the impact of proposed ventilation patterns with a 2-minute physiological sighing on users in ERP-based BCI tasks and investigated the impact of quantitative indicators. Generally, TBR and attention control have negative correlations [ref]. In Figure 2, overall, the proposed ventilation pattern showed a lower TBR than a normal ventilation pattern. This suggests that the proposed respiratory pattern using physiological sighing could enhance attentional control in various situations. In light of this, this provides additional explanatory power for the causal effect between physiological attention alterations and character recognition accuracy. Even though statistical analysis will be required for significance, this result leads our assumptions in an encouraging direction.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Motor Behavior:
Brain Machine Interface 2
Novel Imaging Acquisition Methods:
EEG
Perception, Attention and Motor Behavior:
Attention: Visual 1
Keywords:
Attention Deficit Disorder
Cognition
Electroencephaolography (EEG)
Other - Physiological Sighing
1|2Indicates the priority used for review
Provide references using author date format
[2] R. Fazel-Rezai, B. Allison, C. Guger, E. Sellers, S. Kleih, and A. Kübler, ‘P300 brain computer interface: current challenges and emerging trends’, Frontiers in Neuroengineering, vol. 5, 2012.
[3] C. E. Lakey, D. R. Berry, and E. W. Sellers, ‘Manipulating attention via mindfulness induction improves P300-based brain–computer interface performance’, Journal of Neural Engineering, vol. 8, no. 2, p. 025019, Mar. 2011.
[4] M. Y. Balban et al., ‘Brief structured respiration practices enhance mood and reduce physiological arousal‘, Cell Reports Medicine, vol. 4, no. 1, p. 100895, 2023.
[5] D. Park, Y.-G. Ju, and D.-O. Won, ‘Stable Character Recognition Strategy Using Ventilation Manipulation in ERP-Based Brain-Computer Interface’, in Pattern Recognition, 2023, pp. 15–25.