The challenge of controlling an auditory BCI in the case of severe motor disability.

BACKGROUND: The locked-in syndrome (LIS), due to a lesion in the pons, impedes communication. This situation can also be met after some severe brain injury or in advanced Amyotrophic Lateral Sclerosis (ALS). In the most severe condition, the persons cannot communicate at all because of a complete oculomotor paralysis (Complete LIS or CLIS). This even prevents the detection of consciousness. Some studies suggest that auditory brain-computer interface (BCI) could restore a communication through a « yes-no¼ code. METHODS: We developed an auditory EEG-based interface which makes use of voluntary modulations of attention, to restore a yes-no communication code in non-responding persons. This binary BCI uses repeated speech sounds (alternating "yes" on the right ear and "no" on the left ear) corresponding to either frequent (short) or rare (long) stimuli. Users are instructed to pay attention to the relevant stimuli only. We tested this BCI with 18 healthy subjects, and 7 people with severe motor disability (3 "classical" persons with locked-in syndrome and 4 persons with ALS). RESULTS: We report online BCI performance and offline event-related potential analysis. On average in healthy subjects, online BCI accuracy reached 86% based on 50 questions. Only one out of 18 subjects could not perform above chance level. Ten subjects had an accuracy above 90%. However, most patients could not produce online performance above chance level, except for two people with ALS who obtained 100% accuracy. We report individual event-related potentials and their modulation by attention. In addition to the classical P3b, we observed a signature of sustained attention on responses to frequent sounds, but in healthy subjects and patients with good BCI control only. CONCLUSIONS: Auditory BCI can be very well controlled by healthy subjects, but it is not a guarantee that it can be readily used by the target population of persons in LIS or CLIS. A conclusion that is supported by a few previous findings in BCI and should now trigger research to assess the reasons of such a gap in order to propose new and efficient solutions. CLINICAL TRIAL REGISTRATIONS: No. NCT02567201 (2015) and NCT03233282 (2013).

Active inference as a unifying, generic and adaptive framework for a P300-based BCI.

OBJECTIVE: Going adaptive is a major challenge for the field of brain-computer interface (BCI). This entails a machine that optimally articulates inference about the user's intentions and its own actions. Adaptation can operate over several dimensions which calls for a generic and flexible framework. APPROACH: We appeal to one of the most comprehensive computational approach to brain (adaptive) functions: the active inference (AI) framework. It entails an explicit (probabilistic) model of the user that the machine interacts with, here involved in a P300-spelling task. This takes the form of a discrete input-output state-space model establishing the link between the machine's (i) observations-a P300 or error potential for instance, (ii) representations-of the user intentions to spell or pause, and (iii) actions-to flash, spell or switch-off the application. MAIN RESULTS: Using simulations with real EEG data from 18 subjects, results demonstrate the ability of AI to yield a significant increase in bit rate (17%) over state-of-the-art approaches, such as dynamic stopping. SIGNIFICANCE: Thanks to its flexibility, this one model enables to implement optimal (dynamic) stopping but also optimal flashing (i.e. active sampling), automated error correction, and switching off when the user does not look at the screen anymore. Importantly, this approach enables the machine to flexibly arbitrate between all these possible actions. We demonstrate AI as a unifying and generic framework to implement a flexible interaction behaviour in a given BCI context.

EEG artifact correction strategies for online trial-by-trial analysis.

Brain-machine interfaces (BMIs) use brain signals to control closed-loop systems in real-time. This comes with substantial challenges, such as having to remove artifacts in order to extract reliable features, especially when using electroencephalography (EEG). Some approaches have been described in the literature to address online artifact correction. However, none are being used as a 'gold-standard' method, and no research has been conducted to analyze and compare their respective effects on statistical data analysis (inference-based decision). OBJECTIVE: In this paper, we evaluate methods for artifact correction and describe the necessary adjustments to implement them for online EEG data analysis. APPROACH: We investigate the following methods: artifact subspace reconstruction (ASR), fully online and automated artifact removal for brain-computer interfacing (FORCe), online empirical model decomposition (EMD), and online independent component analysis. For assessment, we simulated online data processing using real data from an auditory oddball task. We compared the above methods with classical offline data processing, in their ability (i) to reveal a significant mismatch negativity (MMN) response to auditory stimuli; (ii) to reveal the more subtle modulation of the MMN by contextual changes (namely, the predictability of the sound sequence), and (iii) to identify the most likely learning process that explains the MMN response. MAIN RESULTS: Our results show that ASR and EMD are both able to reveal a significant MMN and its modulation by predictability, and even appear more sensitive than the offline analysis when comparing alternative models of perception underlying auditory evoked responses. SIGNIFICANCE: ASR and EMD show many advantages when compared to other online artifact correction methods. Besides, subtle modulation analysis of the MMN, embedded in perception computational models is a novel method for assessing the quality of artifact correction methods.

Incorporation of recent waking-life experiences in dreams correlates with frontal theta activity in REM sleep.

Rapid eye movement (REM) sleep and its main oscillatory feature, frontal theta, have been related to the processing of recent emotional memories. As memories constitute much of the source material for our dreams, we explored the link between REM frontal theta and the memory sources of dreaming, so as to elucidate the brain activities behind the formation of dream content. Twenty participants were woken for dream reports in REM and slow wave sleep (SWS) while monitored using electroencephalography. Eighteen participants reported at least one REM dream and 14 at least one SWS dream, and they, and independent judges, subsequently compared their dream reports with log records of their previous daily experiences. The number of references to recent waking-life experiences in REM dreams was positively correlated with frontal theta activity in the REM sleep period. No such correlation was observed for older memories, nor for SWS dreams. The emotional intensity of recent waking-life experiences incorporated into dreams was higher than the emotional intensity of experiences that were not incorporated. These results suggest that the formation of wakefulness-related dream content is associated with REM theta activity, and accords with theories that dreaming reflects emotional memory processing taking place in REM sleep.

Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.

The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.

Improving BCI performance through co-adaptation: applications to the P300-speller.

A well-known neurophysiological marker that can easily be captured with electroencephalography (EEG) is the so-called P300: a positive signal deflection occurring at about 300 ms after a relevant stimulus. This brain response is particularly salient when the target stimulus is rare among a series of distracting stimuli, whatever the type of sensory input. Therefore, it has been proposed and extensively studied as a possible feature for direct brain-computer communication. The most advanced non-invasive BCI application based on this principle is the P300-speller. However, it is still a matter of debate whether this application will prove relevant to any population of patients. In a series of recent theoretical and empirical studies, we have been using this P300-based paradigm to push forward the performance of non-invasive BCI. This paper summarizes the proposed improvements and obtained results. Importantly, those could be generalized to many kinds of BCI, beyond this particular application. Indeed, they relate to most of the key components of a closed-loop BCI, namely: improving the accuracy of the system by trying to detect and correct for errors automatically; optimizing the computer's speed-accuracy trade-off by endowing it with adaptive behavior; but also simplifying the hardware and time for set-up in the aim of routine use in patients. Our results emphasize the importance of the closed-loop interaction and of the ensuing co-adaptation between the user and the machine whenever possible. Most of our evaluations have been conducted in healthy subjects. We conclude with perspectives for clinical applications.

Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing.

Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself.

Impaired pitch perception and memory in congenital amusia: the deficit starts in the auditory cortex.

Congenital amusia is a lifelong disorder of music perception and production. The present study investigated the cerebral bases of impaired pitch perception and memory in congenital amusia using behavioural measures, magnetoencephalography and voxel-based morphometry. Congenital amusics and matched control subjects performed two melodic tasks (a melodic contour task and an easier transposition task); they had to indicate whether sequences of six tones (presented in pairs) were the same or different. Behavioural data indicated that in comparison with control participants, amusics' short-term memory was impaired for the melodic contour task, but not for the transposition task. The major finding was that pitch processing and short-term memory deficits can be traced down to amusics' early brain responses during encoding of the melodic information. Temporal and frontal generators of the N100m evoked by each note of the melody were abnormally recruited in the amusic brain. Dynamic causal modelling of the N100m further revealed decreased intrinsic connectivity in both auditory cortices, increased lateral connectivity between auditory cortices as well as a decreased right fronto-temporal backward connectivity in amusics relative to control subjects. Abnormal functioning of this fronto-temporal network was also shown during the retention interval and the retrieval of melodic information. In particular, induced gamma oscillations in right frontal areas were decreased in amusics during the retention interval. Using voxel-based morphometry, we confirmed morphological brain anomalies in terms of white and grey matter concentration in the right inferior frontal gyrus and the right superior temporal gyrus in the amusic brain. The convergence between functional and structural brain differences strengthens the hypothesis of abnormalities in the fronto-temporal pathway of the amusic brain. Our data provide first evidence of altered functioning of the auditory cortices during pitch perception and memory in congenital amusia. They further support the hypothesis that in neurodevelopmental disorders impacting high-level functions (here musical abilities), abnormalities in cerebral processing can be observed in early brain responses.

Adaptive training session for a P300 speller brain-computer interface.

With a brain-computer interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300 in the oddball paradigm exploited in P300-speller, provides a way to create BCIs by assigning several detected ERP to a command. Due to the noise present in the electroencephalographic signal, the detection of an ERP and its different components requires efficient signal processing and machine learning techniques. As a consequence, a calibration session is needed for training the models, which can be a drawback if its duration is too long. Although the model depends on the subject, the goal is to provide a reliable model for the P300 detection over time. In this study, we propose a new method to evaluate the optimal number of symbols (i.e. the number of ERP that shall be detected given a determined target probability) that should be spelt during the calibration process. The goal is to provide a usable system with a minimum calibration duration and such that it can automatically switch between the training and online sessions. The method allows to adaptively adjust the number of training symbols to each subject. The evaluation has been tested on data recorded on 20 healthy subjects. This procedure lets drastically reduced the calibration session: height symbols during the training session reach an initialized system with an average accuracy of 80% after five epochs.

EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface.

A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory. We propose a new algorithm to select a relevant subset of electrodes by estimating sparse spatial filters. A l(1)-norm penalization term, as an approximation of the l(0)-norm, is introduced in the xDAWN algorithm, which maximizes the signal to signal-plus-noise ratio. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 10 sensors, the loss in classification accuracy is less than 5%.

Topographical dynamics of brain connections for the design of asynchronous brain-computer interfaces.

This article presents a new processing method to design brain-computer interfaces (BCIs). It shows how to use the perturbations of the communication between different cortical areas due to a cognitive task. For this, the network of the cerebral connections is built from correlations between cortical areas at specific frequencies and is analyzed using graph theory. This allows us to describe the topological organisation of the networks using quantitative measures. This method is applied to an auditive steady-state evoked potentials experiment (dichotic binaural listening) and compared to a more classical method based on spectral filtering.

Scalp localization of human auditory cortical activity modified by GSM electromagnetic fields.

PURPOSE: This study attempted to determine whether there is a localized effect of GSM (Global System for Mobile communications) microwaves by studying the Auditory Evoked Potentials (AEP) recorded at the scalp of nine healthy subjects and six epileptic patients. MATERIALS AND METHODS: We determined the influence of GSM RadioFrequency (RF) on parameters characterizing the AEP in time or/and frequency domains. A parameter selection method using SVM (Support Vector Machines)-based criteria allowed us to estimate those most altered by the radiofrequencies. The topography of the parameter modifications was computed to determine the localization of the radiofrequency influence. A statistical test was conducted for selected scalp areas, in order to determine whether there were significant localized alterations due to the RF. RESULTS: The epileptic patients showed a lengthening of the scalp component N100 (100 ms latency) in the frontal area contralateral to the radiation, which may be due to an afferent tract alteration. For the healthy subjects, an amplitude increase of the P200 wave (200 ms latency) was identified in the frontal area. CONCLUSIONS: The present study suggests that radiofrequency fields emitted by mobile phones modify the AEP. Nevertheless, no direct link between these findings and RF-induced damages in brain function was established.

Short-term effects of GSM mobiles phones on spectral components of the human electroencephalogram.

The aim of the study was to investigate whether the GSM (global system for mobile) signals affect the electrical activity of the human brain. Nine healthy subjects and six temporal epileptic patients were exposed to radiofrequencies emitted by a GSM mobile phone signals. Electroencephalographic (EEG) signals were recorded using surface electrodes with and without radiofrequency. In order to obtain a reference, a control session was also carried out. The spectral attributes of the EEG signals recorded by surface electrodes were analyzed. The significant decrease of spectral correlation coefficients under radiofrequency influence showed that the GSM signal altered the spectral arrangement of the EEG activity for healthy subjects as well as epileptic patients. For the healthy subjects, the EEG spectral energy decreased on the studied frequency band [0-40 Hz] and more precisely on occipital electrodes for the alpha-band. For the epileptic patients, these modifications were demonstrated by an increase of the power spectral density of the EEG signal. Nevertheless, these biological effects on the EEG are not sufficient to put forward some electrophysiological hypothesis.