Silent communication: toward using brain signals

From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?     

MEG responses during rhythmic finger tapping in humans to phasic stimulation and their interpretation based on neural mechanisms

 The phase-resetting experiment was applied to human periodic finger tapping to understand how its rhythm is controlled by the internal neural clock that is assumed to exist. In the experiment, the right periodic tapping movement was disturbed transiently by a series of left finger taps in response to impulsive auditory cues presented randomly at various phases within the tapping cycle. After each left finger tap, the original periodic tapping was reestablished within several tapping cycles. Influences of the disturbance on the periodic right finger tapping varied depending on the phase of the periodic right finger tapping at which each left finger tap was made. It was confirmed that the periodic tapping was disturbed not by the auditory cues but by the left finger taps. Based on this fact, in this paper each single left tap was considered as the stimulus, and the phase of the periodic tapping of the right index finger when the left tap was executed as the phase of the stimulus. Responses of the neural activities (magnetoencephalography, MEG), the tapping movement, and the corresponding muscle activities (electromyography) were simultaneously measured. Phase-resetting curves (PRCs) representing the degree of phase reset as a function of the phase of the stimulus were obtained both for the left sensorimotor cortex MEG response and for the right index finger tapping response. The shapes of both PRCs were similar, suggesting that the phase reset of the left sensorimotor cortex activities and that of the finger tapping rhythm were the same. Four out of eight subjects showed type-0 reset in Winfree's definition, and the others showed type-1 reset. For general limit-cycle oscillators, type-0 reset is obtained for relatively strong perturbations and type 1 for weak perturbations. It was shown that the transient response of MEG to the single left tap stimuli in type-0 subjects, where the phase was progressively reset, were different from those in type-1 subjects. Based on detailed analysis of the differences, a neural network model for the phase reset of the tapping rhythm is proposed. Received: 10 February 2000 / Accepted in revised form: 15 January 2002     

Perception of the consequences of self-action is temporally tuned and event driven

It has been proposed that in order to increase the salience of sensations with an external cause, sensations that are predictable based on one's own actions are attenuated [1 and 2]. This may explain why self-imposed tickle [3 and 4] or constant forces [5] are perceived as less intense than the same stimuli externally imposed. Here, subjects used their right index finger to tap a force sensor mounted above their left index finger. When a motor generated a tap on the left finger synchronously with the right tap, simulating contact between the fingers, the perception of force in the left finger was attenuated compared to the same tap experienced during rest. Attenuation gradually reduced as the left tap was either delayed or advanced relative to the active right tap. However, no attenuation was seen to left taps triggered by right-finger movements that stopped above or passed wide of the sensor. We conclude that there is a window of sensory attenuation that is broadly temporally tuned and centered on the time at which the fingers would normally make contact. That is, predictive tactile sensory attenuation is linked to specific external events arising from movement rather than to the movement per se.     

A New Design of Mental State Classification for Subject Independent BCI Systems

BackgroundBrain Computer Interface (BCI) systems have been widely used to develop sustainable assistive technology for people suffering from neurological impairments. A major limitation of current BCI systems is that they are based on Subject-dependent (SD) concept. The SD based BCI system is time consuming and inconvenient for physical or mental disables people and also not suitable for limited computer resources. In order to overcome these problems, recently subject-independent (SI) based BCI concept has been introduced to identify mental states of motor disabled people but the expected outcome of the SI based BCI has not been achieved yet. Hence this paper intends to present an efficient scheme for SI based BCI system. The goal of this research is to develop a method for classifying mental states which can be used by any user. For attaining this target, this study employs a supervised spatial filtering method with four types of feature extraction methods including Katz Fractal Dimension, Sub band Energy, Log Variance and Root Mean Square (RMS) and finally the obtained features are used as input to Linear Discriminant Analysis (LDA) classification model for identifying mental states for SI BCI system.ResultsThe performance of the proposed design is evaluated in several ways such as considering different time window length; different frequency bands; different number of channels. The mean classification accuracy using Katz feature is 84.35% which is the maximum output compare to other features that outperforms the existing methods.ConclusionsOur proposed design will help to make a new technology for development of real-time SI based BCI systems that can be more supportive for the motor disabled patients.     

LASSO based stimulus frequency recognition model for SSVEP BCIs

Steady-state visual evoked potential (SSVEP) has been increasingly used for the study of brain–computer interface (BCI). How to recognize SSVEP with shorter time and lower error rate is one of the key points to develop a more efficient SSVEP-based BCI. To achieve this goal, we make use of the sparsity constraint of the least absolute shrinkage and selection operator (LASSO) for the extraction of more discriminative features of SSVEP, and then we propose a LASSO model using the linear regression between electroencephalogram (EEG) recordings and the standard square-wave signals of different frequencies to recognize SSVEP without the training stage. In this study, we verified the proposed LASSO model offline with the EEG data of nine healthy subjects in contrast to canonical correlation analysis (CCA). In the experiment, when a shorter time window was used, we found that the LASSO model yielded better performance in extracting robust and detectable features of SSVEP, and the information transfer rate obtained by the LASSO model was significantly higher than that of the CCA. Our proposed method can assist to reduce the recording time without sacrificing the classification accuracy and is promising for a high-speed SSVEP-based BCI.     

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.     

Intraspecific variability in the tapping behavior of the deathwatch beetle,Xestobium rufovillosum (Coleoptera: Anobiidae)

Taps were recorded from 46 male and 30 female deathwatch beetles, Xestobium rufovillosum.Beetles tap by striking the frons of the head on the substrate 4–11 times, at a frequency of about 11 Hz. There were no significant differences between the sexes in the number of strikes per tap, or in the frequency of the strikes, although there was significant variation between individuals of both sexes in both of these parameters. Males usually initiate a sequence of taps, to which females reply. Females responded more readily to male beetle taps containing high strike numbers. Use of an artificial tapper showed that females responded most to taps containing at least six strikes. Females did not discriminate between male beetle taps on the basis of strike frequency, although at abnormally high (20-Hz) or low (4-Hz) frequencies produced by the artificial tapper, females were less likely to respond. Male beetles located the source of taps from the artificial tapper and did this more readily with taps of a high strike number. It is not clear why some males produce taps with fewer than six strikes, as these are unlikely to elicit a female response, and so should be the subject of adverse sexual selection.     

Visual Stimuli for P300-Based Brain-Computer Interfaces: Color,Shape, and Mobility

The purpose of this study was to identify the impact of different discriminative features of stimuli in a P300 brain-computer interface paradigm on overall performance and evoked potentials. It has been shown that stimuli sets with a greater number of discriminative features yield better target selection accuracy. Target selection accuracy was significantly higher for the stimuli that differ from each other by color, shape, and semantics. Highest performance was achieved with the stimuli set containing the largest number of discriminative features, namely a set of nine different-colored letters. This result is mainly due to higher mean P300 peak amplitude for stimuli sets that contain more discriminative features. The results of the study can be used for designing a better user experience in brain-computer interfacing (BCI). Motion of the stimuli presentation point and characteristics of this motion (linear or pseudorandom) did not have any impact on BCI performance. This result is promising for future BCI designs with rapid serial visual presentation using mobile robots or augmented reality as stimuli presentation environment.     

The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI

The challenges of research into brain–computer interfaces (BCI) include significant individual differences in learning pace and in the effective operation of BCI devices. The use of neurofeedback training is a popular method of improving the effectiveness BCI operation. The purpose of the present study was to determine to what extent it is possible to improve the effectiveness of operation of sensorimotor rhythm-based brain–computer interfaces (SMR-BCI) by supplementing user training with elements modifying the characteristics of visual feedback. Four experimental groups had training designed to reinforce BCI control by: visual feedback in the form of dummy faces expressing emotions (Group 1); flashing the principal elements of visual feedback (Group 2) and giving both visual feedbacks in one condition (Group 3). The fourth group participated in training with no modifications (Group 4). Training consisted of a series of trials where the subjects directed a ball into a basket located to the right or left side of the screen. In Group 1 a schematic image a face, placed on the controlled object, showed various emotions, depending on the accuracy of control. In Group 2, the cue and targets were flashed with different frequency (4 Hz) than the remaining elements visible on the monitor. Both modifications were also used simultaneously in Group 3. SMR activity during the task was recorded before and after the training. In Group 3 there was a significant improvement in SMR control, compared to subjects in Group 2 and 4 (control). Differences between subjects in Groups 1, 2 and 4 (control) were insignificant. This means that relatively small changes in the training procedure may significantly impact the effectiveness of BCI control. Analysis of behavioural data acquired from all participants at training showed greater effectiveness in directing the object towards the right side of the screen. Subjects with the greatest improvement in SMR control showed a significantly lower difference in the accuracy of rightward and leftward movement than others.     

The Efficiency of the Brain-Computer Interfaces Based on Motor Imagery with Tactile and Visual Feedback

In this study we compared tactile and visual feedbacks for the motor imagery-based brain–computer interface (BCI) in five healthy subjects. A vertical green bar from the center of the fixing cross to the edge of the screen was used as visual feedback. Vibration motors that were placed on the forearms of the right and the left hands and on the back of the subject’s neck were used as tactile feedback. A vibration signal was used to confirm the correct classification of the EEG patterns of the motor imagery of right and left hand movements and the rest task. The accuracy of recognition in the classification of the three states (right hand movement, left hand movement, and rest) in the BCI without feedback exceeded the random level (33% for the three states) for all the subjects and was rather high (67.8% ± 13.4% (mean ± standard deviation)). Including the visual and tactile feedback in the BCI did not significantly change the mean accuracy of recognition of mental states for all the subjects (70.5% ± 14.8% for the visual feedback and 65.9% ± 12.4% for the tactile feedback). The analysis of the dynamics of the movement imagery skill in BCI users with the tactile and visual feedback showed no significant differences between these types of feedback. Thus, it has been found that the tactile feedback can be used in the motor imagery-based BCI instead of the commonly used visual feedback, which greatly expands the possibilities of the practical application of the BCI.     

Estimating the mutual information of an EEG-based Brain-Computer Interface.

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.     

Similar non-random processes maintain diversity in two tropical rainforests.

Quadrat-based analysis of two rainforest plots of area 50 ha, one in Panama (Barro Colorado Island, BCI) and the other in Malaysia (Pasoh), shows that in both plots recruitment is in general negatively correlated with both numbers and biomass of adult trees of the same species in the same quadrat. At BCI, this effect is not significantly influenced by treefall gaps. In both plots, recruitment of individual species is negatively correlated with the numbers of trees of all species in the quadrats, but not with overall biomass. These observations suggest, but do not prove, widespread frequency-dependent effects produced by pathogens and seed-predators that act most effectively in quadrats crowded with trees. Within-species correlations of mortality with numbers or biomass are not found in either plot, indicating that most frequency-dependent mortality takes place before the trees reach 1 cm in diameter. Stochastic effects caused by BCI's more rapid tree turnover may contribute to a larger variance in diversity from quadrat to quadrat at BCI, although they are not sufficient to explain why BCI has fewer than half as many tree species as Pasoh. Finally, in both plots quadrats with low diversity show a significant increase in diversity over time, and this increase is stronger at BCI. This process, like the frequency-dependence, will tend to maintain diversity over time. In general, these non-random forces that should lead to the maintenance of diversity are slightly stronger at BCI, even though the BCI plot is less diverse than the Pasoh plot.     

On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions

This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13–25 Hz, and at high frequencies in the band of 40–60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5–30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12–18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.     

A collaborative brain-computer interface for improving human performance

Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.     

Specific strength and voluntary muscle activation in young and elderly women and men.

The extents to which decreased muscle size or activation are responsible for the decrease in strength commonly observed with aging remain unclear. Our purpose was to compare muscle isometric strength [maximum voluntary contraction (MVC)], cross-sectional area (CSA), specific strength (MVC/CSA), and voluntary activation in the ankle dorsiflexor muscles of 24 young (32 +/- 1 yr) and 24 elderly (72 +/- 1 yr) healthy men and women of similar physical activity level. Three measures of voluntary muscle activation were used: the central activation ratio [MVC/(MVC + superimposed force)], the maximal rate of voluntary isometric force development, and foot tap speed. Men had higher MVC and CSA than did women. Young men had higher MVC compared with elderly men [262 +/- 19 (SE) vs. 197 +/- 22 N, respectively], whereas MVC was similar in young and elderly women (136 +/- 15 vs. 149 +/- 16 N, respectively). CSA was greater in young compared with elderly subjects. There was no age-related impairment of specific strength, central activation ratio, or the rate of voluntary force development. Foot tap speed was reduced in elderly (34 +/- 1 taps/10 s) compared with young subjects (47 +/- 1 taps/10 s). These results suggest that isometric specific strength and the ability to fully and rapidly activate the dorsiflexor muscles during a single isometric contraction were unimpaired by aging. However, there was an age-related deficit in the ability to perform rapid repetitive dynamic contractions.     

A Co-Adaptive Brain-Computer Interface for End Users with Severe Motor Impairment

Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.     

Clustering Gene Expression Regulators: New Approach to Disease Subtyping

One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.     

Discrete and Effortful Imagined Movements Do Not Specifically Activate the Autonomic Nervous System

Background

The autonomic nervous system (ANS) is activated in parallel with the motor system during cyclical and effortful imagined actions. However, it is not clear whether the ANS is activated during motor imagery of discrete movements and whether this activation is specific to the movement being imagined. Here, we explored these topics by studying the baroreflex control of the cardiovascular system.

Methodology/Principal Findings

Arterial pressure and heart rate were recorded in ten subjects who executed or imagined trunk or leg movements against gravity. Trunk and leg movements result in different physiological reactions (orthostatic hypotension phenomenon) when they are executed. Interestingly, ANS activation significantly, but similarly, increased during imagined trunk and leg movements. Furthermore, we did not observe any physiological modulation during a control mental-arithmetic task or during motor imagery of effortless movements (horizontal wrist displacements).

Conclusions/Significance

We concluded that ANS activation during motor imagery is general and not specific and physiologically prepares the organism for the upcoming effortful action.     

Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface

In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD) was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods.     

Computer simulation of different types of evolutionary process

The computer model of two alternative variants of biological evolution is proposed. The first variant supposes random while the second--directed change of individual features, thus corresponding to the Darwinian and non-Darwinian evolution. The evolution of fish communities in fresh waters serves as a particular example. The model is executed using object-oriented method of programming and mathematical apparatus of fuzzy logics. The investigation of the model showed that process of Darwinian evolution is connected with significantly greater species diversity and variability of evolutionary process trajectories than non-Darwinian one. On the other hand, non-Darwinian type of evolution provides fast achievement of high individual fitness, especially under conditions of constant environment. Non-Darwinian type evolution failed in big evolutionary alteration (for example, transition to predation); while the Darwinian evolution under the same conditions can produce such alterations though it took more time and many extinct species. Phylogenetic tree of Darwinian evolution is always more complex than of non-Darwinian one under the same conditions.