Active sampling in the olfactory domain is an important aspect of mouse behaviour, and there is increasing evidence that respiration-entrained neural activity outside of the olfactory system sets an important global brain rhythm. It is therefore important to accurately measure breathing during natural behaviours. We develop a new approach to do this in freely moving animals, by implanting a telemetry-based pressure sensor into the right jugular vein, which allows for wireless monitoring of thoracic pressure. After verifying this technique against standard head-fixed respiration measurements, we combined it with EEG and EMG recording and used evolving partial coherence analysis to investigate the relationship between respiration and brain activity across a range of experiments in which the mice could move freely. During voluntary exploration of odours and objects, we found that the association between respiration and cortical delta and theta rhythms decreased, while the association between respiration and cortical alpha rhythm increased. During sleep, however, the presentation of an odour was able to cause a transient increase in sniffing without changing dominant sleep rhythms (delta and theta) in the cortex. Our data align with the emerging idea that the respiration rhythm could act as a synchronising scaffold for specific brain rhythms during wakefulness and exploration, but suggest that respiratory changes are less able to impact brain activity during sleep. Combining wireless respiration monitoring with different types of brain recording across a variety of behaviours will further increase our understanding of the important links between active sampling, passive respiration, and neural activity.
Establishing a Relationship Between VOMS Performance and Changes in EEG Signatures for a Rapid Assessment and Diagnosis of Concussions
This study aims to discover a possible relationship between electroencephalogram (EEG) signature changes as physiological indicators of one’s current state, and performance on the Vestibular Ocular Motor Screening (VOMS) assessment. A Muse 2 generated a baseline EEG scan for each participant, allowing for the collection of data associated with one’s brain activity. The subjects were then taken through several VOMS domain tests with a continued recording by the device. A comparable analysis was conducted between the participant’s baseline recording and VOMS recording with an intent to identify the consistent correlations in between. In conclusion the findings of this study show potential for characteristic brain activity patterns dependent upon what VOMS domain is being tested. Therefore, when any deviations from those features are observed, the likelihood of the presence of a concussion is much greater.
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- Award ID(s):
- 1827769
- PAR ID:
- 10314103
- Editor(s):
- Kalra, Jay Lightner
- Date Published:
- Journal Name:
- International Conference on Applied Human Factors and Ergonomics AHFE 2021: Advances in Human Factors and Ergonomics in Healthcare and Medical Devices pp 101-108
- Volume:
- 263
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-80744-3_13
