M. A. Lopez-Gordo

Dry EEG Electrodes

Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.

AN AUDITORY BRAIN-COMPUTER INTERFACE WITH ACCURACY PREDICTION

Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.

Use of Phase in Brain---Computer Interfaces based on Steady-State Visual Evoked Potentials

Brain–computer interfaces based on steady-state visual evoked potentials (SSVEP-BCIs) extract the amplitude of these potentials for classification. The use of the phase has not yet been widely used in on-line classification, since it requires a very accurate real time system that keeps synchronized the stimulation, recording and processing. In this paper, it has been presented an experiment, based on the AM modulation of flickering stimuli, that demonstrates that first, the phase shifts of different stimuli can be recovered from that of the corresponding SSVEPs without the need of a real time system; second, this information can be used efficiently to develop a BCI based on the classification of the phase shifts of the SSVEPs. Since the phase is statistically independent of the amplitude, the joint use in classification of both would improve the performance of this type of BCI.

An auditory brain–computer interface evoked by natural speech

Brain–computer interfaces (BCIs) are mainly intended for people unable to perform any muscular movement, such as patients in a complete locked-in state. The majority of BCIs interact visually with the user, either in the form of stimulation or biofeedback. However, visual BCIs challenge their ultimate use because they require the subjects to gaze, explore and shift eye-gaze using their muscles, thus excluding patients in a complete locked-in state or under the condition of the unresponsive wakefulness syndrome. In this study, we present a novel fully auditory EEG-BCI based on a dichotic listening paradigm using human voice for stimulation. This interface has been evaluated with healthy volunteers, achieving an average information transmission rate of 1.5 bits min⁻¹ in full-length trials and 2.7 bits min⁻¹ using the optimal length of trials, recorded with only one channel and without formal training. This novel technique opens the door to a more natural communication with users unable to use visual BCIs, with promising results in terms of performance, usability, training and cognitive effort.

Customized stimulation enhances performance of independent binary SSVEP-BCIs.

OBJECTIVE Brain-computer interfaces based on steady-state visual evoked potentials (SSVEP-BCIs) achieve the highest performance, due to their multiclass nature, in paradigms in which different visual stimuli are shown. Studies of independent binary SSVEP-BCIs have been previously presented in which it was not necessary to gaze at the stimuli at the cost of performance. Despite that, the energy of the SSVEPs is largely affected by the temporal and spatial frequencies of the stimulus, there are no studies in the BCI literature about its combined impact on the final performance of SSVEP-BCIs. The objective of this study is to present an experiment that evaluates the best configuration of the visual stimulus for each subject, thus minimizing the decline in performance of independent binary SSVEP-BCIs. METHODS The participants attended and ignored a single structured stimulus configured with a combination of spatial and temporal frequencies at a time. They were instructed to gaze at a central point during the whole experiment. The best combination of spatial and temporal frequencies achieved for each subject, in terms of signal-to-noise ratio (SNR), was subsequently determined. RESULTS The SNR showed a significant dependency on the combination of frequencies, in such a way that only a reduced set of these combinations was applicable for obtaining an optimum SNR. The selection of an inappropriate stimulus configuration may cause a degradation of the information transmission rate (ITR) as it does the SNR. CONCLUSIONS The appropriate selection of the optimal spatial and temporal frequencies determines the performance of independent binary SSVEP-BCIs. This fact is critical to enhance its low performance; hence, they should be adjusted independently for each subject. SIGNIFICANCE Independent binary SSVEP-BCIs can be used in patients who are unable to control their gaze sufficiently. The correct selection of the spatial and temporal frequencies has a considerable benefit on their low ITR that must be taken into account. In order to find the most suitable frequencies, a test similar to the presented in this study should be performed beforehand for each SSVEP-BCI user. This regard is not documented in studies conducted in the BCI literature.

A high performance SSVEP-BCI without gazing

Brain-computer interfaces based on steady-state visual evoked potential (SSVEP-BCIs) achieve a high performance due to their multiclass nature in paradigms where gazing is needed. Studies of binary SSVEP-BCIs have been presented without the need of gazing at the expense of low performance. This study presents a high performance binary SSVEP-BCI that allows an efficient communication without the need of gazing. The information transmission rate (ITR) obtained was 0.64±0.27 bits per seconds with peaks of 1.16 bits per second and an accuracy of 90±7%, which is an excellent performance for a binary SSVEP-BCI. To achieve this performance two main factors were involved: on the one hand, the use of both amplitude and phase of the SSVEP in the classification and, on the other, the use of the absence of gaze (“thousand-yard stare”) as a way of helping to ignore the stimulus.

A BINARY PHASE-SHIFT KEYING RECEIVER FOR THE DETECTION OF ATTENTION TO HUMAN SPEECH

Synthetic sounds, tone-beeps, vowels or syllables are typically used in the assessment of attention to auditory stimuli because they evoke a set of well-known event-related potentials, whose characteristics can be statistically contrasted. Such approach rules out the use of stimuli with non-predictable response, such as human speech. In this study we present a procedure based on the robust binary phase-shift keying (BPSK) receiver that permits the real-time detection of selective attention to human speeches in dichotic listening tasks. The goal was achieved by tagging the speeches with two barely-audible tags whose joined EEG response constitutes a reliable BPSK constellation, which can be detected by means of a BPSK receiver. The results confirmed the expected generation of the BPSK constellation by the human auditory system. Also, the bit-error rate and the information transmission rate achieved in the detection of attention fairly followed the expected curves and equations of the standard BPSK receiver. Actually, it was possible to detect attention as well as the estimation a priori of its accuracy based on the signal-to-noise ratio of the BPSK signals. This procedure, which permits the detection of the attention to human speeches, can be of interest for new potential applications, such as brain-computer interfaces, clinical assessment of the attention in real time or for entertainment.

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.

Phase-shift keying of EEG signals

Numerous algorithms have tried the blind separation of multiple auditory sources in the framework of the cocktail party phenomenon with relative success. However, this is a straightforward goal for humans, who use selective attention to filter one target out of many irrelevant sources. In this paper we present a paradigm capable of detecting attention in multitalker scenarios. The paradigm inserts in messages barely-audible tags whose evoked brain response constitutes a M-ary Phase-shift keying (M-PSK) constellation. Then, detection is performed based on the M-PSK receiver. In two experimental sessions, we inserted tags in four and six auditory messages to elicit 4-PSK and 6-PSK constellations, respectively. We instructed thirteen participants to attend only one message and ignore the rest (forced-attention paradigm). The results presented significant detection of the attended messages above the chance level with mean accuracies of 0.47 and 0.32 for 4-PSK and 6-PSK, respectively. The results support that our paradigm detected attention to one target speech in multitalker scenarios. This outcome could be applied for the assessment of auditory attention, as assistive technology for attention impairment, in neuro-marketing or Brain-computer interfaces. We propose an M-PSK model for the detection of auditory attention in a multitalker scenario.We generate an M-PSK constellation from EEG signals.For a robust detection, we embed attention in M-PSK symbols.Our approach extends dichotic listening task to 4 and 6 talkers.Methodology could be applied in neuromarketing, attention assessment and for BCIs.

BCI-based navigation in virtual and real environments

A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.