Hybrid EEG-fNIRS asynchronous brain-computer interface for multiple motor tasks

Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm—Left-Arm—Right-Hand—Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes

Productivity Improvement at a High-tech State-owned Industry--An Indonesian Case Study of Employee Motivation

The purpose of this case study was to identify the level of employee motivation at an Indonesian high-tech state-owned company. Comparisons were drawn between labor and management as well as Indonesian and Western industrial environments. The overall results provide insight into employee motivation and the potential for productivity improvement that should prove beneficial to management at state-owned and privately owned companies in Indonesia and the Pacific Rim. The study can also help Westerners appreciate culture differences and productivity challenges in this developing country

Use of dry-electroencephalogram and support vector for objective pain assessment

Our primary goal was to objectively quantify pain. The experiment we designated for this task was via dry electroencephalography (EEG) in conjunction with a support vector machine classifier (SVM). Normal gel-based electrode EEG has been validated as reliable in pain measurement. Yet, to date, there are few documented trials that use dry-EEG for pain quantification. In addition, SVM classifiers have proven accurate when classifying pain intensity. Therefore, we believe EEG combined with SVM could increase the statistical power of pain assessment. However, due to the subjectivity of pain, currently clinicians mainly rely on verbal reports. This research could offer a method to objectively monitor pain, eliminate observer error and individualize treatment

Tracking team mental workload by multimodal measurements in the operating room

Mental workload and its effects on surgical performance are underexplored topics, despite their importance for operating room (OR) efficiency and patient safety. We developed a multimodal platform that can simultaneously collect data from EEG, heart rate and breathing rate, tool handle pressure, and eye tracker from mobile subjects. We performed experiments using the Fundamentals of Laparoscopic Surgery model, with 22 subjects of varying skill levels ranging from nonsurgeon to expert. The results indicated significant modulations of the measurements depending on pupil size, heart rate variability, P300 response, tool pressure, task difficulty, time-on-task, and skill level. These provide evidence that physiology based metrics can be used in automated classification of fine gradations of skill, the assessment and certification of surgery trainees, developing real-time flags and warnings for the OR, and validating new OR technology

Diverse monogenic subforms of human spermatogenic failure

Non-obstructive azoospermia (NOA) is the most severe form of male infertility and typically incurable. Defining the genetic basis of NOA has proven challenging, and the most advanced classification of NOA subforms is not based on genetics, but simple description of testis histology. In this study, we exome-sequenced over 1000 clinically diagnosed NOA cases and identified a plausible recessive Mendelian cause in 20%. We find further support for 21 genes in a 2-stage burden test with 2072 cases and 11,587 fertile controls. The disrupted genes are primarily on the autosomes, enriched for undescribed human knockouts , and, for the most part, have yet to be linked to a Mendelian trait. Integration with single-cell RNA sequencing data shows that azoospermia genes can be grouped into molecular subforms with synchronized expression patterns, and analogs of these subforms exist in mice. This analysis framework identifies groups of genes with known roles in spermatogenesis but also reveals unrecognized subforms, such as a set of genes expressed across mitotic divisions of differentiating spermatogonia. Our findings highlight NOA as an understudied Mendelian disorder and provide a conceptual structure for organizing the complex genetics of male infertility, which may provide a rational basis for disease classification

Cross-validating models of continuous data from simulation and experiment by using linear regression and artificial neural networks

We are increasingly surrounded by sensors gathering massive amounts of data, and patterns in continuous variables are often discovered by using artificial neural networks (ANN), while linear regression (LR) is useful for detecting linear relationships. LR also provide preliminary estimates of potentially complex associations, and serve as a benchmark for the performance of ANNs. We show that while cross-validation (CV) is indispensable for insuring the robustness of the discovered patterns, it systematically leads, when combined with LR, to specific artefacts that underestimate the extent of the associations between predictor and target variables. We explain how this previously unnoticed type of artefact arises specifically from the combination of CV with LR and does not affect non-linear methods such as ANN. We also demonstrate through simulations that ANN were able to discover a wide range of complex associations missed by LR. The results were confirmed by the analysis of physiological, behavioural and subjective data collected from N=31 human subjects performing laparoscopy training experiments

Physiological correlates of cognitive load in laparoscopic surgery

Laparoscopic surgery can be exhausting and frustrating, and the cognitive load experienced by surgeons may have a major impact on patient safety as well as healthcare economics. As cognitive load decreases with increasing proficiency, its robust assessment through physiological data can help to develop more effective training and certification procedures in this area. We measured data from 31 novices during laparoscopic exercises to extract features based on cardiac and ocular variables. These were compared with traditional behavioural and subjective measures in a dual-task setting. We found significant correlations between the features and the traditional measures. The subjective task difficulty, reaction time, and completion time were well predicted by the physiology features. Reaction times to randomly timed auditory stimuli were correlated with the mean of the heart rate (0.29 r =−) and heart rate variability (0.4 r =). Completion times were correlated with the physiologically predicted values with a correlation coefficient of 0.84. We found that the multi-modal set of physiology features was a better predictor than any individual feature and artificial neural networks performed better than linear regression. The physiological correlates studied in this paper, translated into technological products, could help develop standardised and more easily regulated frameworks for training and certification

Population coding by globally coupled phase oscillators

A system of globally coupled phase oscillators subject to an external input is considered as a simple model of neural circuits coding external stimulus. The information coding efficiency of the system in its asynchronous state is quantified using Fisher information. The effect of coupling and noise on the information coding efficiency in the stationary state is analyzed. The relaxation process of the system after the presentation of an external input is also studied. It is found that the information coding efficiency exhibits a large transient increase before the system relaxes to the final stationary state.Comment: 7 pages, 9 figures, revised version, new figures added, to appear in JPSJ Vol 75, No.

A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: brain computer interface system using EEG signals and artificial intelligence

This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods

High density optical neuroimaging predicts surgeons's subjective experience and skill levels

Measuring cognitive load is important for surgical education and patient safety. Traditional approaches of measuring cognitive load of surgeons utilise behavioural metrics to measure performance and surveys and questionnaires to collect reports of subjective experience. These have disadvantages such as sporadic data, occasionally intrusive methodologies, subjective or misleading self-reporting. In addition, traditional approaches use subjective metrics that cannot distinguish between skill levels. Functional neuroimaging data was collected using a high density, wireless NIRS device from sixteen surgeons (11 attending surgeons and 5 surgery resident) and 17 students while they performed two laparoscopic tasks (Peg transfer and String pass). Participant’s subjective mental load was assessed using the NASA-TLX survey. Machine learning approaches were used for predicting the subjective experience and skill levels. The Prefrontal cortex (PFC) activations were greater in students who reported higher-than-median task load, as measured by the NASA-TLX survey. However in the case of attending surgeons the opposite tendency was observed, namely higher activations in the lower v higher task loaded subjects. We found that response was greater in the left PFC of students particularly near the dorso- and ventrolateral areas. We quantified the ability of PFC activation to predict the differences in skill and task load using machine learning while focussing on the effects of NIRS channel separation distance on the results. Our results showed that the classification of skill level and subjective task load could be predicted based on PFC activation with an accuracy of nearly 90%. Our finding shows that there is sufficient information available in the optical signals to make accurate predictions about the surgeons’ subjective experiences and skill levels. The high accuracy of results is encouraging and suggest the integration of the strategy developed in this study as a promising approach to design automated, more accurate and objective evaluation methods