Jesus Minguillon

Trends in EEG-BCI for daily-life: Requirements for artifact removal

Abstract Since the discovery of the EEG principles by Berger in the 20’s, procedures for artifact removal have been essential in its pre-processing. In literature, diverse approaches based on signal processing, data mining, statistic models, and others compile information from multiple electrodes to build filters for artifact removal in the time, frequency or space domains. For almost one century, EEG acquisitions have required strict experimental conditions that included an isolated room, clinical acquisition systems, rigorous experimental protocols and very precise stimulation control. Under these steady experimental conditions, artifact removal techniques have not significantly evolved since then. However, in the last decade technological advances in brain-computer interfaces permit EEG acquisition by means of wireless, mobile, dry, wearable, and low-cost EEG headsets, with new potential daily-life applications, such as in entertainment or industry. New aspects not considered before, such as massive muscular and electrical artifacts, reduced number of electrodes, uncontrolled concomitant stimulus or the need for online processing are now essential. In this paper, we present a critical review of EEG artifact removal approaches, discuss their applicability to daily-life EEG-BCI applications, and give some directions and guidelines for upcoming research in this topic. Based on the results of the review, existing artifact removal techniques need further evolution to be applied in daily-life EEG-BCI. The use of multiple-step procedures is recommended, combining source decomposition with blind source separation and adaptive filtering, rather than using them separately. It is also recommendable to define and characterize most of artifacts evoked in daily-life EEG-BCI for a more effective removal.

Stress Assessment by Prefrontal Relative Gamma

Stress assessment has been under study in the last years. Both biochemical and physiological markers have been used to measure stress level. In neuroscience, several studies have related modification of stress level to brain activity changes in limbic system and frontal regions, by using non-invasive techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). In particular, previous studies suggested that the exhibition or inhibition of certain brain rhythms in frontal cortical areas indicates stress. However, there is no established marker to measure stress level by EEG. In this work, we aimed to prove the usefulness of the prefrontal relative gamma power (RG) for stress assessment. We conducted a study based on stress and relaxation periods. Six healthy subjects performed the Montreal Imaging Stress Task (MIST) followed by a stay within a relaxation room while EEG and electrocardiographic signals were recorded. Our results showed that the prefrontal RG correlated with the expected stress level and with the heart rate (HR; 0.8). In addition, the difference in prefrontal RG between time periods of different stress level was statistically significant (p < 0.01). Moreover, the RG was more discriminative between stress levels than alpha asymmetry, theta, alpha, beta, and gamma power in prefrontal cortex. We propose the prefrontal RG as a marker for stress assessment. Compared with other established markers such as the HR or the cortisol, it has higher temporal resolution. Additionally, it needs few electrodes located at non-hairy head positions, thus facilitating the use of non-invasive dry wearable real-time devices for ubiquitous assessment of stress.

EEG topographies provide subject-specific correlates of motor control

Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is debated. Evidence of such a functional role of spontaneous brain activity would provide support for the design of novel and sensitive biomarkers in neurological disorders. Here we examined whether and to what extent intrinsic brain activity contributes and plays a functional role during natural motor behaviors. For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers. We show that, in every subject, well-known resting-state microstates persist during movement execution with similar topographies and temporal characteristics, but are supplemented by novel task-related microstates. We then show that the subject-specific microstates’ dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy. These findings provide first evidence that spontaneous brain activity encodes detailed information about motor control, offering as such the prospect of a novel tool for the definition of subject-specific biomarkers of brain plasticity and recovery in neuro-motor disorders.

Human Neuro-Activity for Securing Body Area Networks: Application of Brain-Computer Interfaces to People-Centric Internet of Things

A former definition states that a brain-computer Interface provides a direct communication channel to the brain without the need for muscles and nerves. With the emergence of wearable and wireless brain-computer interfaces, these systems have evolved to become part of wireless body area networks, offering people-centric applications such as cognitive workload assessment and detection of selective attention. Currently, wireless body area networks are mostly integrated by low-cost devices that, because of their limited hardware resources, cannot generate secure random numbers for encryption. This is a critical issue in the context of new Internet of Things device communication and its security. Such devices require securing their communication, mostly by means of the automatic renewal of the cryptographic keys. In the domain of the people-centric Internet of Things, we propose to use wireless brain-computer interfaces as a secure source of entropy, based on neuro- activity, capable to generate secure keys that outperforms other generation methods. In our approach, current wireless brain-computer interface technology is an attractive option to offer novel services emerged from novel necessities in the context of the people-centric Internet of Things. Our proposal is an implementation of the human-in-the-loop paradigm, in which devices and humans indistinctly request and offer services to each other for mutual benefit.

Detection of attention in multi-talker scenarios

A fuzzy-based m-PSK attention detector for multi-talker scenarios is proposed.The approach outperformed the performance of previous works (ITR and accuracy).This outcome could have relevant impact on BCI community. The automatic and online detection of auditory attention in multi-talker scenarios (e.g., cocktail party paradigm) is a current topic in electroencephalography (EEG)-based brain-computer interfaces (BCIs). Recent works have demonstrated a way to make it possible by means of a model based on an m-ary phase shift keying (m-PSK) detector. However, this attention detection model lacks of relevant information such as the non-stationary nature of EEG signals, the neuro-plasticity/habituation effects or the nonlinearities of the attention. In this paper we propose an enriched version of the attention detection model constituted by an automatic adaptive m-PSK detector implemented on fuzzy logic. In it, the relevant information mentioned before is modeled as two inputs that feed the fuzzy-based attention detection model. The output provides the detection. Our enriched model outperformed the results of previous works in terms of mean information transfer rate (ITR) (4-PSK: 5.41bpm; 6-PSK: 6.03bpm) and accuracy (4-PSK: 0.54; 6-PSK: 0.39) after only 4.63 (4-PSK) and 2.93 (6-PSK) seconds of processing. The proposed model for the automatic detection of auditory attention can have relevant impact on several areas such as education, public transport, jobs, industry, attention disorders, ubiquitous systems, sports and art.

A Mobile Brain-Computer Interface for Clinical Applications: From the Lab to the Ubiquity

Technological advances during the last years have contributed to the development of wireless and low-cost electroencephalography (EEG) acquisition systems and mobile brain-computer interface (mBCI) applications. The most popular applications are general-purpose (e.g., games, sports, daily-life, etc.). However, clinical usefulness of mBCIs is still an open question. In this paper we present a low-cost mobile BCI application and demonstrate its potential utility in clinical practice. In particular, we conducted a study in which visual evoked potentials (VEP) of two subjects were analyzed using our mBCI application, under different conditions: inside a laboratory, walking and traveling in a car. The results show that the features of our system (level of synchronization, robustness and signal quality) are acceptable for the demanding standard required for the electrophysiological evaluation of vision. In addition, the mobile recording and cloud computing of VEPs offers a number of advantages over traditional in-lab systems. The presented mobile application could be used for visual impairment screening, for ubiquitous, massive and low-cost evaluation of vision, and as ambulatory diagnostic tool in rural or undeveloped areas.

Blue lighting accelerates post-stress relaxation: Results of a preliminary study

Several authors have studied the influence of light on both human physiology and emotions. Blue light has been proved to reduce sleepiness by suppression of melatonin secretion and it is also present in many emotion-related studies. Most of these have a common lack of objective methodology since results and conclusions are based on subjective perception of emotions. The aim of this work was the objective assessment of the effect of blue lighting in post-stress relaxation, in comparison with white lighting, by means of bio-signals and standardized procedures. We conducted a study in which twelve healthy volunteers were stressed and then performed a relaxation session within a chromotherapy room with blue (test group) or white (control group) lighting. We conclude that the blue lighting accelerates the relaxation process after stress in comparison with conventional white lighting. The relaxation time decreased by approximately three-fold (1.1 vs. 3.5 minutes). We also observed a convergence time (3.5–5 minutes) after which the advantage of blue lighting disappeared. This supports the relationship between color of light and stress, and the observations reported in previous works. These findings could be useful in clinical and educational environments, as well as in daily-life context and emerging technologies such as neuromarketing. However, our study must be extended to draw reliable conclusions and solid scientific evidence.

Portable System for Real-Time Detection of Stress Level

Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.

Securing Passwords Beyond Human Capabilities with a Wearable Neuro-Device

The election of strong passwords is a challenging task for humans that could undermine the secure online subscription to services in mobile applications. Composition rules and dictionaries help to choose stronger passwords, although at the cost of the easiness to memorize them. When high-performance computers are not available, such as in mobile scenarios, the problem is even worse because mobile devices typically lack good enough entropy sources. Then, the goal is to obtain strong passwords with the best efficiency in terms of level of entropy per character unit. In this study, we propose the use neuro-activity as source of entropy for the efficient generation of strong passwords. In our experiment we used the NIST test suite to compare binary random sequences extracted from neuro-activity by means of a mobile brain-computer interface with (i) strong passwords manually generated with restrictions based on dictionary and composition rules and (ii) passwords generated automatically by a mathematical software running on a work station. The results showed that random sequences based on neuro-activity were much more suitable for the generation of strong passwords than those generated by humans and were as strong as those generated by a computer. Also, the rate at which random bits were generated by neuro-activity (4 Kbps) was much faster than the passwords manually generated. Thus, just a very small fraction of the time and cognitive workload caused to manually generate a password has enough entropy for the generation of stronger, shorter and easier to remember passwords. We conclude that in either mobile scenarios or when good enough entropy sources are not available the use of neuro-activity is an efficient option for the generation of strong passwords.

Setting the Parameters for an Accurate EEG (Electroencephalography)-Based Emotion Recognition System

The development of a suitable EEG-based emotion recognition system has become a target in the last decades for BCI (Brain Computer Interface) applications. However, there are scarce algorithms and procedures for real time classification of emotions. In this work we introduce a new approach to select the appropriate parameters in order to build up a real-time emotion recognition system. We recorded the EEG-neural activity of 5 participants while they were looking and listening to an audiovisual database composed by positive and negative emotional video clips. We tested 11 different temporal window sizes, 6 ranges of frequency bands and 5 areas of interest located mainly on prefrontal and frontal brain regions. The most accurate time window segment was selected for each participant, giving us probable positive and negative emotional characteristic patterns, in terms of the most informative frequency-location pairs. Our preliminary results provide a reliable way to establish the more appropriate parameters to develop an accurate EEG-based emotion classifier in real-time.