We present a brain-computer interface (BCI) version of the famous “Connect Four”. Target selection is based on brain event-related responses measured with nine EEG sensors. Two players compete against each other using their brain activity only. Importantly, we turned the general difficulty of producing a reliable BCI command into an advantage, by extending the game play and rules, in a way that adds fun to the game and might well prove to trigger up motivation in future studies. The principle of this new BCI is directly inspired from our own implementation of the classical P300 Speller (Maby et al. 2010, Perrin et al. 2011). We here establish a proof of principle that the same electrophysiological markers can be used to design an efficient two-player game. Experimental evaluation on two competing healthy subjects yielded an average accuracy of 82%, which is in line with our previous results on many participants and demonstrates that the BCI “Connect Four” can effectively be controlled. Interestingly, the duration of the game is not significantly affected by the usual slowness of BCI commands. This suggests that this kind of BCI games could be of interest to healthy players as well as to disabled people who cannot play with classical games.

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.

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.

$\dot{\mathrm{V}}$O₂max is recognized as a key measure in exercise physiology and sports medicine. However, only 20–50% of maximal incremental exercise tests (IET) result in a plateau of $\dot{\mathrm{V}}$O₂ ($\dot{\mathrm{V}}$O₂pl). To our knowledge, no study has yet examined the possible difference in brain activity during an IET, in $\dot{\mathrm{V}}$O₂pl and non-plateau athletes with the same $\dot{\mathrm{V}}$O₂max and age. This study aimed to shed light on the central governor hypothesis, namely that the inability to reach a $\dot{\mathrm{V}}$O₂pl may be dictated by the brain rather than by a peripheral physical limit. This hypothesis can now be explored using electroencephalography (EEG) during IET, measuring concomitant power in specific frequency bands. Forty-two athletes were divided into two groups: those who practiced endurance sports and those who did not, and were asked to perform an IET. EEG signals and gas exchange were recorded. A $\dot{\mathrm{V}}$O₂pl was observed in twenty-two subjects (52%). EEG power increased in all subjects during IET, except in the alpha band, which showed variability, but not significantly (64% increase, 34% decrease, p = 0.07). No differences were found between endurance athletes and non-endurance athletes, except for $\dot{\mathrm{V}}$O₂max (60.10 ± 6.16 vs. 51.77 ± 6.41, p < 0.001). However, the baseline-corrected ratio of EEG power to $\dot{\mathrm{V}}$O₂ was found to decrease in all subjects during IET, in the alpha, beta and theta bands. In conclusion, the presence or absence of a $\dot{\mathrm{V}}$O₂pl is not related to the type of EEG response during an IET. Nevertheless, the decline in brain and $\dot{\mathrm{V}}$O₂ powers/ratios in all frequency bands suggests that aerobic power may be constrained by brain mobilization.

Background: Exercise duration at maximum oxygen uptake ($\dot{\mathrm{V}}$O₂max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at $\dot{\mathrm{V}}$O₂max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach $\dot{\mathrm{V}}$O₂max correlated with Alpha/$\dot{\mathrm{V}}$O₂ (p < 0.001), Alpha/$\dot{\mathrm{V}}$CO₂ (p < 0.001), and Beta/$\dot{\mathrm{V}}$CO₂ (p = 0.002). The time spent at $\dot{\mathrm{V}}$O₂max correlated with Theta/$\dot{\mathrm{V}}$O₂ (p = 0.002) and Theta/$\dot{\mathrm{V}}$CO₂ (p < 0.001). The time-to-exhaustion was correlated with Theta/$\dot{\mathrm{V}}$CO₂ (p < 0.001) and Alpha/$\dot{\mathrm{V}}$CO₂ (p < 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance.