Biology subjects

Jung, T.-P.

Publications and source records attributed to Jung, T.-P..

3 recordsLinked to original sources

bioRxiv2021

Near-Zero Phase-Lag Hyperscanning in a Novel Wireless EEG System

Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with wireless electroencephalography (EEG) is increasingly popular owing to its mobility and ability to decipher social interactions in natural settings at the millisecond scale. To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposed a clock-synchronized method using a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional RF channels to keep the counter of their receiving dongles updated, guaranteeing that binding event markers received by the dongle with the EEG time series have the correct timestamp. The results of two simulation experiments and one video gaming experiment revealed that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase-lag and negligible amplitude difference between signals. According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.

bioengineering↗

bioRxiv2021

Mindfulness Training Alters Resting-State EEG Dynamics in Novice Practitioners via Mindful Breathing and Body-scan

Mindfulness-based stress reduction (MBSR) has been proven to improve mental health and quality of life. This study examined how mindfulness training and various types of mindfulness practices altered brain activity. Specifically, the spectral powers of scalp electroencephalography (EEG) of the MBSR group who underwent an 8-week mindfulness training--including mindful breathing and body-scan--were evaluated and compared with those of the waitlist controls. Empirical results indicated that the long-term mindfulness intervention effect significantly elevated the resting-state beta powers and reduced resting-state delta powers in both practices; such changes were not observed in the waitlist control. Compared with mindful breathing, body-scanning resulted in an overall decline in EEG spectral powers at both delta and gamma bands among trained participants. Together with our preliminary data of expert mediators, the aforementioned spectral changes were salient after intervention, but mitigated along with expertise. Additionally, after receiving training, the MBSR groups mindfulness and emotion regulation levels improved significantly, which were correlated with the EEG spectral changes in the theta, alpha, and low-beta bands. This study elaborated the neurophysiological correlates of mindfulness practices, suggesting that MBSR might function as a unique internal processing that involves increased vigilant capability and induces alterations similar to other cognitive training.

neuroscience↗

bioRxiv2020

Unsupervised Learning of Brain State Dynamics during Emotion Imagery using High-Density EEG

Here we assume that emotional states correspond to functional dynamic states of brain and body, and attempt to characterize the appearance of these states in high-density scalp electroencephalographic (EEG) recordings acquired from 31 participants during 1-2 hour sessions, each including fifteen 3-5 min periods of self-induced emotion imagination using the method of guided imagery. EEG offers an objective and high-resolution measurement of whatever portion of cortical electrical dynamics is resolvable from scalp recordings. Despite preliminary progress in EEG-based emotion decoding using supervised machine learning methods, few studies have applied data-driven, unsupervised decomposition approaches to investigate the underlying EEG dynamics by characterizing brain temporal dynamics during emotional experience. This study applies an unsupervised approach - adaptive mixture independent component analysis (adaptive mixture ICA, AMICA) that learns a set of ICA models each accounted for portions of a given multi-channel EEG recording. We demonstrate that 20-model AMICA decomposition can identify distinct EEG patterns or dynamic states active during each of the fifteen emotion-imagery periods. The transition in EEG patterns revealed the time-courses of brain-state dynamics during emotional imagery. These time-courses varied across emotions: "grief" and "happiness" showed more abrupt transitions while "contentment" was nearly indistinguishable from the preceding rest period. The spatial distributions of independent components (ICs) of the AMICA models showed higher similarity within-subject across emotions than within-emotion across subjects. No significant differences in IC distributions were found between positive and negative emotions. However, significant changes in IC distributions during emotional imagery compared to rest were identified in brain areas such as the left prefrontal cortex, the posterior cingulate cortex, the motor cortex, and the visual cortex. The study demonstrates the feasibility of AMICA in modeling high-density and nonstationary EEG and its utility in providing data-driven insights into brain state dynamics during self-paced emotional experiences, which have been difficult to measure. This approach can advance our understanding of highly dynamical emotional processes and improve the performance of EEG-based emotion decoding for affective computing and human-computer interaction.

neuroscience↗