Stress is an emergency response of our organism. Although stress may be necessary in case of life threatening situations, most of the stress experienced by modern day human is misplaced, and spur from evolutionary pressure that is not relevant in our societies. Finding ways to monitor, control, and reduce stress has become critical for the well-being of human societies. For the purpose of developing real time applications to assess and reduce stress for the general public using portable devices, we designed an experiment to trigger stress in laboratory conditions. Our results show that we successfully induced stress and that heart-related measures such as heart rate, heart rate variability and spectral estimation based on heart beats were reliable indicators of stress.

Using a wireless single channel EEG device, we investigated the feasibility of using shortterm frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65% - 75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.

We propose to use real-time single-channel EEG signal to quantify user enjoyment level elicited by media content. Selected time-frequency components from single-channel frontal EEG were extracted and formed a statistical multivariate model predicting user enjoyment level. Frequency components from Theta, Alpha and Beta bands at different time moments were selected. We found robust model performance during 10-folds cross-validation with 100 repetitions. A high correlation of around 0.8 between predicted and actual enjoyment level of subjects was achieved. Considering various factors of the selected features, we found an important role of alpha as the emotional component, and beta as the cognitive component involved in the complex enjoyment processes. Also, in accordance with the peak-end rule, feature from latter part of the video seems to create a large influence to the overall experience. From all of these results, we implement real-time EEG-based detection system for media user enjoyment with single EEG channel.

This paper presents an investigation into the cortico-muscular relationship during a grasping task by evaluating the information transfer between EEG and EMG signals. Information transfer was computed via a non-linear model-free measure, transfer entropy (TE). To examine the cross-frequency interaction, TEs were computed after the times series were decomposed into various frequency ranges via wavelet transform. Our results demonstrate the capability of TE to capture the direct interaction between EEG and EMG. In addition, the cross-frequency analysis revealed instantaneous decrease in information transfer from EEG to the high frequency component of EMG (100-200Hz) during the onset of movement.

This paper presents a novel wearable single-channel electrooculography (EOG) based human-computer interface (HCI) with a simple system design and robust performance. In the proposed system, EOG signals for control are generated from double eye blinks, collected by a commercial wearable device (the NeuroSky MindWave headset), and then converted into a sequence of commands that can control cursor navigations and actions. The EOG-based cursor control system was tested on 8 subjects in indoor or outdoor environment, and the average accuracy is 84.42% for indoor uses and 71.50% for outdoor uses. Compared with other existing EOG-based HCI systems, this system is highly user-friendly and does not require any training. Therefore, this system has the potential to provide an easy-to-use and cheap assistive technique for locked-in patients who have lost their main body muscular abilities but with proper eye-condition.

Objective: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities.Methods: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (+/- 9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6 x 6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects.Results: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN).Conclusions: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate).Significance: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

This brief introduces a new and practical human-machine interface (HMI) system based on single-channel electrooculography (EOG) signals. The proposed system uses a consumer wireless recording device to collect EOG and employs new encoding/decoding paradigms to convey users' intentions with EOG from eye movements including blinking and looking up. The simplicity and mobility of the system provides a comfortable and practical solution to HMI. Furthermore, to reduce the hardware complexity and power consumption of the signal processing modules of the EOG-based HMI system, a novel multiplierless implementation is developed, where all the algorithms involved, such as bandpass filtering, wavelet filtering, and support vector machine, can be realized using a limited number of adders and shifters only. Experimental results show that the proposed system offers a simple, practical, and yet reliable EOG-based HMI with low complexity and power consumption.

Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 x 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (+/- 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (+/- 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R <5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.

This study explores the feasibility of mental workload monitoring using a single-channel mobile EEG system. We investigated the modulation of frontal neural activity with respect to changes in mental workload levels induced by visual-motor tasks of varied difficulty. Using a computerized visual-motor task similar to mirror drawing, our work demonstrated that perceived difficulty was more dominated by the complexity of the path to be traced than the actual time taken to complete the task. EEG activities recorded from the forehead area at the beginning of each trial are positively correlated with overall perceived difficulty of the task. Results in this study suggest that frontal EEG spectra are significantly modulated by the changes in relative mental workload levels during a visual-motor task. Such finding shed light on the possibility of mental workload monitoring in daily life using a user-friendly mobile EEG system.

This study investigates the modulation of frontal EEG dynamics with respect to progress in motor learning. Using a computerized visual-motor task similar to mirror drawing, our work demonstrated that overall EEG activities in all frequency bands decreased with an increase in motor task familiarity. In particular, frontal EEG activities in delta band of the whole trial and gamma band at the beginning of each trial are having a significant negative relationship with the overall familiarity level of the task. The findings suggest that frontal EEG spectra are significantly modulated during motor skill acquisition.

This study investigates the modulation of frontal EEG dynamics with respect to progress in motor skill acquisition using a wireless EEG system with a single dry sensor. Participants were required to complete repeated trials of a computerized visual-motor task similar to mirror drawing while the EEG was collected. In each trial, task performance of the participants was summarized with a familiarity index which took into account the performance accuracy, completion rate and time. Our findings demonstrated that certain EEG power spectra decreased with an increase in motor task familiarity. In particular, frontal EEG activities in delta and theta bands of the whole trial and in gamma band in the middle of the trial are having a significant negative relationship with the overall familiarity level of the task. The findings suggest that frontal EEG spectra are significantly modulated during motor skill acquisition. Results of this study shed light on the possibility of simultaneous monitoring of brain activity during an unconstrained natural task with a single dry sensor mobile EEG in an everyday environment.

The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain–computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5–8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.

Brain-computer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development have grown explosively over the past two decades. Efforts have recently begun to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this review, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology, and identify potential users and potential applications. Finally, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.