Failure to breathe persists without air hunger or alarm following amygdala seizures.

Postictal apnea is thought to be a major cause of sudden unexpected death in epilepsy (SUDEP). However, the mechanisms underlying postictal apnea are unknown. To understand causes of postictal apnea, we used a multimodal approach to study brain mechanisms of breathing control in 20 patients (ranging from pediatric to adult) undergoing intracranial electroencephalography for intractable epilepsy. Our results indicate that amygdala seizures can cause postictal apnea. Moreover, we identified a distinct region within the amygdala where electrical stimulation was sufficient to reproduce prolonged breathing loss persisting well beyond the end of stimulation. The persistent apnea was resistant to rising CO2 levels, and air hunger failed to occur, suggesting impaired CO2 chemosensitivity. Using es-fMRI, a potentially novel approach combining electrical stimulation with functional MRI, we found that amygdala stimulation altered blood oxygen level-dependent (BOLD) activity in the pons/medulla and ventral insula. Together, these findings suggest that seizure activity in a focal subregion of the amygdala is sufficient to suppress breathing and air hunger for prolonged periods of time in the postictal period, likely via brainstem and insula sites involved in chemosensation and interoception. They further provide insights into SUDEP, may help identify those at greatest risk, and may lead to treatments to prevent SUDEP.

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging.

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Clinical efficacy of EMDR in unipolar depression: Changes in theta cordance.

Eye Movement Desensitization and Reprocessing (EMDR) has demonstrated efficacy in treating major depressive disorder. EMDR increases cerebral perfusion in the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC). Activity in the ACC and dlPFC can be measured by theta cordance (TC) but has not been examined in EMDR. Ten participants (3 men, 7 women, M age = 42.31 ± 15.03) received ten 75 ± 15 minute EMDR sessions over 6.5 ± .5 weeks. Results indicated that PHQ-9 depression scores reduced from T1 (M = 13.9 ± 3.31) to T11 (M = 6.30 ± 3.23) with EMDR (SMD = 2.30), and that fTC but not pfTC was significantly related to this change. Depression declined as fTC declined. EMDR may engage the dlPFC or ACC that modulates depression and aid in reducing fTC and thus depression levels.

Affective brain-computer interfaces: Choosing a meaningful performance measuring metric.

Affective brain-computer interfaces are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. To assess the effectiveness of EEG recordings for recognizing affective states, we used data collected in our lab as well as the publicly available DEAP database. We also reviewed the articles that used the DEAP database and found that a significant number of articles did not consider the presence of the class imbalance in the DEAP. Failing to consider class imbalance creates misleading results. Further, ignoring class imbalance makes the comparison of the results between studies using different datasets impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level, hence it is vital to consider class bias while determining if the results are above chance. To properly account for the effect of class imbalance, we suggest the use of balanced accuracy as a performance metric, and its posterior distribution for computing credible intervals. For classification, we used features from the literature as well as theta beta-1 ratio. Results from DEAP and our data suggest that the beta band power, theta band power, and theta beta-1 ratio are better feature sets for classifying valence, arousal, and dominance, respectively.

A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation.

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.

Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.

P300 Latency Estimation Using Least Mean Squares Filter.

Event-related potentials (ERPs) are the brain response directly related to specific events or stimuli. The P300 ERP is a positive deflection nominally 300ms post-stimulus that is related to mental decision making processes and also used in P300-based speller systems. Single-trial estimation of P300 responses will help to understand the underlying cognitive process more precisely and also to improve the speed of speller brain-computer interfaces (BCIs). This paper aims to develop a single-trial estimation of the P300 amplitudes and latencies by using the least mean squares (LMS) adaptive filtering method. Results for real data from people with amyotrophic lateral sclerosis (ALS) have shown that the LMS filter can be effectively used to estimate P300 latencies.

Enhancing P300-BCI performance using latency estimation.

Brain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three second-level classifiers were tested: least squares (LS), step-wise linear discriminant analysis (SWLDA), and support vector machine (SVM). Of these three, LS and SWLDA performed better than the original online classifier. The resulting combination demonstrated improved detection of brain responses for many participants, resulting in better BCI performance. Interestingly, the performance gain was greatest for those individuals for whom the BCI did not work well online, indicating that this method may be most suitable for improving performance of otherwise marginal participants.