BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.

The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation.

Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as ±23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.

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.

The Effects of Word Priming on Emotion Classification from Neurological Signals.

Affective states play an important role in human behavior and decision-making. In recent years, several affective brain-computer interface (aBCI) studies have focused on developing an emotion classifier based on elicited emotions within the user. However, it is difficult to achieve consistency in elicited emotions across populations, which can lead to dataset imbalances. The experimental design presented in this paper seeks to avoid consistency issues by asking the participant to classify the emotion portrayed in images of facial expressions, rather than their own emotions. Priming is also a common technique used in psychology studies that is known to influence emotional perception. To improve participant accuracy, we investigated matching and mis-matched word priming for the facial expression images. Electro-encephalogram (EEG) data were used to generate images fed into a classifier based on the Big Transfer model, BiT-M R101x1. The primed images resulted in higher classification accuracy overall. Further, by building different classifier models for both mis-matched primed images and matching primed images, we were able to achieve classification accuracies above 90%. We also provided the classifier with the true labels of the photographs instead of the labels generated by the participants and achieved similar results. The experimental paradigm of measuring brain activity during the emotional classification of another individual provides consistently high, balanced classification accuracies.

BCI Accuracy Using Classifier-Based Latency Estimation and the Optimal Interstimulus Interval.

PURPOSE: Detection of event-related potentials (ERPs) in brain-computer interfaces (BCIs) allow for communication by individuals with neuromuscular disorders. Enhancing BCI accuracy may be possible through the exploration of the optimal interstimulus interval (ISI). Our objective is to investigate the relationship between BCI accuracy and the optimal ISI value for an individual. APPROACH: Using the previously developed classifier-based latency estimation (CBLE) [1], we investigated the relationship between the interstimulus interval (ISI) and P3 Speller BCI accuracy. Participants underwent two consecutive sessions in one day. The first session had a default ISI value of 120ms. An optimal ISI value calculated from the first session was used in the second. RESULTS: Ten subjects participated in the study. Of the ten, half received an optimal ISI value of 120ms and half 160ms. Accuracy differences after implementing the adjusted ISI ranged from -26.1 percent to 4.35 percent. Suggestions for additional experimental design adjustments are highlighted under the discussion portion of this manuscript.

Applying Big Transfer-based classifiers to the DEAP dataset.

Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.

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.

Evaluation of the Long-Term Storage Stability of the Cyanide Antidote: Dimethyl Trisulfide and Degradation Product Identification.

This study reports the long-term storage stability of a formulation of the cyanide (CN) antidote dimethyl trisulfide (DMTS). The F3-formulated DMTS was stored in glass ampules at 4, 22, and 37 °C. Over a period of one year, nine ampules (n = 3 at each temperature) were analyzed by high-performance liquid chromatography (HPLC)-UV/vis at daily time intervals in the first week, weekly time intervals in the first month, and monthly thereafter for a period of one year to determine the DMTS content. No measurable loss of DMTS was found at 4 and 22 °C, and good stability was noted up to five months for samples stored at 37 °C. At 37 °C, a 10% (M/M) decrease of DMTS was discovered at the sixth month and only 30% (M/M) of DMTS remained by the end of the study; discoloration of the formulation and the growth of new peaks in the HPLC chromatogram were also observed. To identify the unknown peaks at 37 °C, controlled oxidation studies were performed on DMTS using two strong oxidizing agents: meta-chloroperoxybenzoic acid (mCPBA) and hydrogen peroxide (H2O2). Dimethyl tetrasulfide and dimethyl pentasulfide were observed as products using both of the oxidizing agents. Dimethyl disulfide was also observed as a product of degradation, which was further oxidized to S-methyl methanethiosulfonate only when mCPBA was used. HPLC-UV/vis and gas chromatography-mass spectrometry/solid phase microextraction analysis revealed good agreement between the degradation products of the stability study at 37 °C and those of disproportionation reactions. Furthermore, at 4 and 22 °C, chromatograms were remarkably stable over the one-year study period, indicating that the F3-formulated DMTS shows excellent long-term storage stability at T ≤ 22 °C.

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.

Smart bed based daytime behavior prediction in Children with autism spectrum disorder - A Pilot Study.

Monitoring the sleep patterns of children with autism spectrum disorders (ASD) and understanding how sleep quality influences their daytime behavior is an important issue that has received very limited attention. Polysomnography (PSG) is commonly used as a gold standard for evaluating sleep quality in children and adults. However, the intrusive nature of sensors used as part of PSG can themselves affect sleep and is, therefore, not suitable for children with ASD. In this study, we evaluate an unobtrusive and inexpensive bed system for in-home, long-term sleep quality monitoring using ballistocardiogram (BCG) signals. Using the BCG signals from this smart bed system, we define "restlessness" as a surrogate sleep quality estimator. Using this sleep feature, we build predictive models for daytime behavior based on 1-8 previous nights of sleep. Specifically, we use two supervised machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). For all daytime behaviors, we achieve more than 78% and 79% accuracy of correctly predicting behavioral issues with both SVM and ANN classifiers, respectively. Our findings indicate the usefulness of our designed bed system and how the restlessness feature can improve the prediction performance.

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters.

BACKGROUND: The goal of this work was to create a sharable dataset of heart-driven signals, including ballistocardiograms (BCGs) and time-aligned electrocardiograms (ECGs), photoplethysmograms (PPGs), and blood pressure waveforms. METHODS: A custom, bed-based ballistocardiographic system is described in detail. Affiliated cardiopulmonary signals are acquired using a GE Datex CardioCap 5 patient monitor (which collects ECG and PPG data) and a Finapres Medical Systems Finometer PRO (which provides continuous reconstructed brachial artery pressure waveforms and derived cardiovascular parameters). RESULTS: Data were collected from 40 participants, 4 of whom had been or were currently diagnosed with a heart condition at the time they enrolled in the study. An investigation revealed that features extracted from a BCG could be used to track changes in systolic blood pressure (Pearson correlation coefficient of 0.54 +/- 0.15), dP/dtmax (Pearson correlation coefficient of 0.51 +/- 0.18), and stroke volume (Pearson correlation coefficient of 0.54 +/- 0.17). CONCLUSION: A collection of synchronized, heart-driven signals, including BCGs, ECGs, PPGs, and blood pressure waveforms, was acquired and made publicly available. An initial study indicated that bed-based ballistocardiography can be used to track beat-to-beat changes in systolic blood pressure and stroke volume. SIGNIFICANCE: To the best of the authors' knowledge, no other database that includes time-aligned ECG, PPG, BCG, and continuous blood pressure data is available to the public. This dataset could be used by other researchers for algorithm testing and development in this fast-growing field of health assessment, without requiring these individuals to invest considerable time and resources into hardware development and data collection.

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.

Sealing Effects on the Storage Stability of the Cyanide Antidotal Candidate, Dimethyl Trisulfide.

BACKGROUND: Dimethyl trisulfide (DMTS) is a highly lipid-soluble cyanide (CN) antidote candidate molecule. In prior studies with various US FDA-approved co-solvents, surfactants, and their combinations, aqueous solutions containing 15% polysorbate 80 (Poly80) were found to effectively solubilize DMTS in formulations for intramuscular administration. However, DMTS formulated in 15% aqueous Poly80 solutions showed gradual losses over time when stored in vials with septum-based seals. OBJECTIVE: The present study tested whether storing DMTS formulations in hermetically sealed glass ampules could mitigate storage losses. METHODS: Samples consisted of 1-mL aliquots of a 50 mg/ml stock solution of DMTS in 15% aqueous Poly80. The control samples were stored using a vial-within-a-vial system-the inner and outer vials were sealed respectively, with a snap cap, and with a crimped septum. The hermetically sealed test samples were stored in fire-sealed glass ampules. The DMTS content was measured by HPLC-UV analysis at specific time points over a 100-day period. RESULTS: While the control samples exhibited systematic DMTS losses, no DMTS losses were observed from the test samples stored in hermetically sealed glass ampules over the 100-day testing period. CONCLUSION: DMTS formulated in 15% aqueous Poly80 solution has excellent stability when stored in fire-sealed glass ampules and thus has the potential to be effectively stored as an intramuscular CN countermeasure for mass casualty scenarios.

Methemoglobin Forming Effect of Dimethyl Trisulfide in Mice.

Dimethyl trisulfide (DMTS) is a natural organic trisulfide that has been patented as a promising antidotal candidate against cyanide (CN). The primary mode of action of DMTS is as a sulfur donor that enables the conversion of CN to thiocyanate. Recently, it was discovered that DMTS is capable of oxidizing hemoglobin (Hb) to methemoglobin (MetHb) in vitro. The goal of these experiments was to measure the extent of DMTS-induced MetHb formation in vivo. In these experiments, intramuscular (IM) injections of formulated DMTS were administered to mice. Following the IM injection, blood was drawn and analyzed for MetHb using a rapid spectrophotometric method. Methemoglobin levels peaked in a dose-dependent manner between 20 and 30 min., and then began dropping. The highest MetHb levels measured for the 50, 100, 200 and 250 mg/kg doses of DMTS were respectively 3.28, 6.12, 9.69, and 10.76% MetHb. These experiments provide the first experimental evidence that IM administered DMTS generates MetHb in vivo and provide additional evidence for the presence of a secondary therapeutic pathway for DMTS - CN scavenging by DMTS-generated MetHb.

From the Cover: In Vitro and In Vivo Blood-Brain Barrier Penetration Studies with the Novel Cyanide Antidote Candidate Dimethyl Trisulfide in Mice.

Recent in vitro and in vivo studies highlight the strong potential of dimethyl trisulfide (DMTS) as an antidote for cyanide (CN) intoxication. Due to its high oxygen demand, the brain is one of the main target organs of CN. The blood-brain barrier (BBB) regulates the uptake of molecules into the brain. In the literature, there is no data about the ability of DMTS to penetrate the BBB. Therefore, our aim was to test the in vitro BBB penetration of DMTS and its in vivo pharmacokinetics in blood and brain. The in vitro BBB penetration of DMTS was measured by using a parallel artificial membrane permeability assay (BBB-PAMPA), and a triple BBB co-culture model. The pharmacokinetics was investigated in a mouse model by following the DMTS concentration in blood and brain at regular time intervals following intramuscular administration. DMTS showed high penetrability in both in vitro systems (apparent permeability coefficients: BBB-PAMPA 11.8 × 10-6 cm/s; cell culture 158 × 10-6 cm/s) without causing cell toxicity and leaving the cellular barrier intact. DMTS immediately absorbed into the blood after the intramuscular injection (5 min), and rapidly penetrated the brain of mice (10 min). In addition to the observed passive diffusion in the in vitro studies, the contribution of facilitated and/or active transport to the measured high permeability of DMTS in the pharmacokinetic studies can be hypothesized. Earlier investigations demonstrating the antidotal efficacy of DMTS against CN together with the present results highlight the promise of DMTS as a brain-protective CN antidote.

Reaction of Dimethyl Trisulfide with Hemoglobin.

Dimethyl trisulfide (DMTS) is a promising antidotal candidate for cyanide intoxication. DMTS acts as a sulfur donor in the conversion of cyanide to the less-toxic thiocyanate. The alternate reaction pathways of DMTS in the blood are not well understood. We report changes in the hemoglobin absorption spectrum upon reaction with DMTS. These changes closely match those induced by the known methemoglobin former, sodium nitrite. The kinetics of methemoglobin formation with DMTS is slower than with sodium nitrite. These results support the hypothesis that a potentially significant side-reaction of the therapeutically administered DMTS is the oxidization of hemoglobin to methemoglobin.

Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control.

OBJECTIVE: Challenges in improving the performance of dexterous upper-limb brain-machine interfaces (BMIs) have prompted renewed interest in quantifying the amount and type of sensory information naturally encoded in the primary motor cortex (M1). Previous single unit studies in monkeys showed M1 is responsive to tactile stimulation, as well as passive and active movement of the limbs. However, recent work in this area has focused primarily on proprioception. Here we examined instead how tactile somatosensation of the hand and fingers is represented in M1. APPROACH: We recorded multi- and single units and thresholded neural activity from macaque M1 while gently brushing individual finger pads at 2 Hz. We also recorded broadband neural activity from electrocorticogram (ECoG) grids placed on human motor cortex, while applying the same tactile stimulus. MAIN RESULTS: Units displaying significant differences in firing rates between individual fingers (p < 0.05) represented up to 76.7% of sorted multiunits across four monkeys. After normalizing by the number of channels with significant motor finger responses, the percentage of electrodes with significant tactile responses was 74.9% ± 24.7%. No somatotopic organization of finger preference was obvious across cortex, but many units exhibited cosine-like tuning across multiple digits. Sufficient sensory information was present in M1 to correctly decode stimulus position from multiunit activity above chance levels in all monkeys, and also from ECoG gamma power in two human subjects. SIGNIFICANCE: These results provide some explanation for difficulties experienced by motor decoders in clinical trials of cortically controlled prosthetic hands, as well as the general problem of disentangling motor and sensory signals in primate motor cortex during dextrous tasks. Additionally, examination of unit tuning during tactile and proprioceptive inputs indicates cells are often tuned differently in different contexts, reinforcing the need for continued refinement of BMI training and decoding approaches to closed-loop BMI systems for dexterous grasping.

Method development for detecting the novel cyanide antidote dimethyl trisulfide from blood and brain, and its interaction with blood.

The antidotal potency of dimethyl trisulfide (DMTS) against cyanide poisoning was discovered and investigated in our previous studies. Based on our results it has better efficacy than the Cyanokit and the Nithiodote therapies that are presently used against cyanide intoxication in the US. Because of their absence in the literature, the goal of this work was to develop analytical methods for determining DMTS from blood and brain that could be employed in future pharmacokinetic studies. An HPLC-UV method for detection of DMTS from blood, a GC-MS method for detection of DMTS from brain, and associated validation experiments are described here. These analytical methods were developed using in vitro spiking of brain and blood, and are suitable for determining the in vivo DMTS concentrations in blood and brain in future pharmacokinetic and distribution studies. An important phenomenon was observed in the process of developing these methods. Specifically, recoveries from fresh blood spiked with DMTS were found to be significantly lower than recoveries from aged blood spiked in the same manner with DMTS. This decreased DMTS recovery from fresh blood is important, both because of the role it may play in the antidotal action of DMTS in the presence of cyanide, and because it adds the requirement of sample stabilization to the method development process. Mitigation procedures for stabilizing DMTS samples in blood are reported.