Neurological Evidence of Diverse Self-Help Breathing Trainings with Virtual Reality and Bio-Feedback Assistance: an Extensive Exploration of EEG Markers
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 53
- 试验地点
- 2
- 主要终点
- EEG effective connectivity(dDTF)
研究概览
简要总结
The goal of this research is to learn about the neuro-mechanism beneath breath training, mindfulness meditation, or periods of idleness. This research also focuses on the use of virtual reality (VR) and bio-feedback (BF) integrated assistance system in breath training, and seeks for the potential of generalizing breath training in public.
The main questions it aims to answer are:
Whether and how people's neuro-mechanism (indicated by EEG indexes) changes when they are performing different breath training techniques (i.e., mindful breathing, guided breathing, and breath counting).
Researchers will compare the neuro-markers when participants perform different styles of breath training.
Participants will:
- Participants will equip an EEG system, a VR headset, a respiratory belt, and a heartbeat sensor.
- Participants will perform resting state task, mindful breathing task, guided breath task, and breath counting task respectively.
- EEG activity, breath rate, reaction time, accuracy, and HRV will be recorded. Each session will last approximately two hours.
详细描述
- Research Overview In today's fast-paced industrial society, managing individual physical and psychological stress has become crucial for maintaining mental, emotional, and brain health. Practices such as breath training, mindfulness meditation, or periods of idleness have been suggested as effective means to relieve stress, reduce anxiety, and improve sleep quality. This study combines cognitive psychology behavioral measurements, electroencephalography (EEG), and heart rate variability (HRV) to investigate the cognitive function, brain structure, and brain activity of adults with breath training/mindfulness meditation experience or regular episodes of mind-wandering. Meanwhile, this research also focuses on the use of technology, namely virtual reality (VR) and bio-feedback (BF) integrated assistance system, and seeks for the potential of generalizing breath training in public. By utilizing non-invasive neuroimaging techniques, this research aims to provide scientific evidence regarding the effects of breath training/mindfulness meditation and mind-wandering. Ultimately, the study seeks to apply these findings to improve national mental and brain health.
The research questions are: whether and how people's neuro-mechanism (indicated by EEG indexes) changes when they are performing different breath training techniques (i.e., mindful breathing, guided breathing, and breath counting). 2. Research Participants Age range: 20-80 years old. Total: 53 participants.
Withdrawal Criteria:
Participants can withdraw at any point if they feel discomfort. 3. Compensation for Participation: Participants will receive a total of NT$1,000. 4. Informed Consent Process The research personnel will explain the study to participants at the study locations. The informed consent process will take approximately 30 minutes per participant. 5. Potential Side Effects, Follow-Up Procedures, and Necessary Rehabilitation Plans
- Physiological Risks:
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Basic Science
- 盲法
- None
入排标准
- 年龄范围
- 20 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Healthy adults aged between 20 and
- •Normal vision or corrected-to-normal vision.
排除标准
- •History of epilepsy, brain injury, or other neurological disorders in the individual or their family.
- •Long-term use of medication (e.g., antidepressants, sleep aids).
- •Claustrophobia.
结局指标
主要结局
EEG effective connectivity(dDTF)
时间窗: Through study completion, an average of 1 hour
This study employed the direct directed transfer function (dDTF) to evaluate causal relationships between EEG channels. Modified from the directed transfer function, the dDTF is an effective connectivity estimator grounded in frequency-domain Granger causality. The dDTF isolates and assesses the direct causal link between a specific pair of channels, effectively mitigating the impact of indirect neural influences because of brain tissue conductivity.
EEG connectivity inflow
时间窗: Through study completion, an average of 1 hour
For a given EEG channel, the connectivity inflow represents the cumulative sum of all corresponding incoming connectivity edges.
EEG connectivity outflow
时间窗: Through study completion, an average of 1 hour
For a given EEG channel, the connectivity outflow represents the cumulative sum of all corresponding outgoing connectivity edges.
EEG band power
时间窗: Through study completion, an average of 1 hour
We converted the EEG data to a frequency-domain signal using a short-time Fourier transformation. All transformed spectra were then log-transformed and represented in dB (10log10).
次要结局
未报告次要终点
研究者
Hei-Yin Hydra Ng
Principal Investigator
National Tsing Hua University