On the stimulus duty cycle in steady state visual evoked potential

Brain-computer interfaces (BCI) are useful devices that allow direct control of external devices using thoughts, i.e. brain's electrical activity. There are several BCI paradigms, of which steady state visual evoked potential (SSVEP) is the most commonly used due to its quick response and accuracy. SSVEP stimuli are typically generated by varying the luminance of a target for a set number of frames or display events. Conventionally, SSVEP based BCI paradigms use magnitude (amplitude) information from frequency domain but recently, SSVEP based BCI paradigms have begun to utilize phase information to discriminate between similar frequency targets. This paper will demonstrate that using a single frame to modulate a stimulus may lead to a bi-modal distribution of SSVEP as a consequence of a user attending both transition edges. This incoherence, while of less importance in traditional magnitude domain SSVEP BCIs becomes critical when phase is taken into account. An alternative modulation technique incorporating a 50% duty cycle is also a popular method for generating SSVEP stimuli but has a unimodal distribution due to user's forced attention to a single transition edge. This paper demonstrates that utilizing the second method results in significantly enhanced performance in information transfer rate in a phase discrimination SSVEP based BCI

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

The heat pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is an element with high probability of failure due to the fact that it is an outside construction and also due to its size. In the present study, a novel intelligent system was designed to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements of one year. It was based on classification techniques with the aim of detecting failures in real time. Then, the model was validated and verified over the building; it obtained good results in all the operating conditions ranges

Comparing Recalibration Strategies for Electroencephalography-Based Decoders of Movement Intention in Neurological Patients with Motor Disability

Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention

A study on the utility of temporal derivatives and unsupervised clustering in brain-computer interfaces

Brain-computer interfaces (BCIs) rely on accurate classification of event-related potentials (ERP), a task commonly delegated to a machine-learning algorithm, which investigates features derived from the voltages (V) recorded at different scalp locations with the electro-encephalogram (EEG). The performance of the machine-learning algorithm is an area that has captured the interest of the research community. Although major advancements have been made, BCIs suffer from uncertainties that arise from assumptions such as that participants are “focused”, “still” and that no unpredictable events occurred during the recording, for example abrupt sounds or light changes. From the range of possible uses of BCIs, one of the most challenging is its adaptation to everyday life situations. Addressing both participant and environmental related influences to the EEG could enable the usage of BCIs outside the confines of the laboratory. In addition, in order to create a BCI that can act as an “enhancement” for the able-bodied requires a way to identify recurrent events without prior knowledge, thus providing the user with a way to increment the “understanding” of his BCI. Moreover, information such as location, latency and shape of recurring events could provide solid grounds for future researchers to build upon. In the thesis the above problem is challenged by investigating two main topics: assuming that the neuro-signals are additive (i.e. uncorrelated), (a) the usage of the first time derivative of V (dV) as feature regarding performance in classification of an ERP, and (b) unsupervised clustering of ERPs. Both investigations tackle the problem of mining properties of unknown neuro-signals. Theoretical investigations carried out on in each topic are performed using synthetic signals to assess the expected behaviour. Using real data from a P300 BCI mouse, both topics were evaluated; the classification performance of dV was found to be significantly better than V while evaluating a baseline for comparison. Having such a positive outcome encouraged an attempt to create a single linkage unsupervised clustering method based on statistical significance. Without knowing if an ERP was generated or not, the developed clustering algorithm, based on dV, is shown to be accurate in identifying the shape of the underlying, “unknown” ERP. For years researchers have been constructing experiments to uncover EEG events directly related to stimuli. An outcome of this research is that recurring EEG responses which might have been neglected, simply because they were not expected, are now identifiable

Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences

With aging, the preference for positive stimuli increases compared to negative stimuli. This is called “positivity effect” and it may be observed in both behavior and brain activity. The main goal of this work was to characterized age effects on emotional processing to improve our understood of this positivity effect. The second goal was to evaluate in which conditions these effects could make older people more vulnerable when they are confronted to threatening situations. A first EEG study revealed that the attentional engagement decreased with age for negative stimuli, regardless of their activation level, in an affective categorization task. Conversely, the processing of positive stimuli was preserved with age and, consequently, a reduction of the negativity bias was observed. In a second EEG study, using a similar paradigm to study 1 with the exception of the task which was an “action tendency task”, we observed a preservation of the negativity bias. A third study revealed that the voluntary attention on interest situations for aged adults (positive) and on appraisal process modulated with age was requisite to observe positivity effects. Parallel to this work, a new method was proposed to recognize and classify emotional states based on EEG signals. We obtained encouraging results which suggest the possibility to use this method to elaborate brain-computer interfaces to protect old people against a potential vulnerability due to positivity effect. Taken together, these results demonstrate that positivity effect is due to motivational shifts with age. Older people would be motivated to increase their well-being and would regulate their emotions by reducing the impact of negative stimuli, provided no other more important motivations are absent.Dans le vieillissement « sain », la préférence pour les stimuli positifs augmente par rapport aux stimuli négatifs. Ce phénomène est appelé « effet de positivité » et peut être observé au niveau comportemental et cérébral. L'objectif principal de cette thèse a été de caractériser les effets de l'âge sur les traitements émotionnels afin d'améliorer notre compréhension des effets de positivité. L'objectif sous-jacent a été d'évaluer dans quelles conditions ces effets peuvent conduire à une plus grande vulnérabilité des personnes âgées face à des situations menaçantes. Une première étude en électroencéphalographie a révélé que l'engagement attentionnel pour des scènes naturelles négatives diminue avec l'âge quel que soit leur niveau d'activation dans une tâche de catégorisation affective. A l'inverse, ce dernier reste inchangé pour les situations positives, conduisant à une réduction des biais de négativité. Une deuxième étude en électroencéphalographie, dont le paradigme était similaire à la première étude, a mis en évidence que les biais de négativité restent préservés avec l'âge lorsque l'évaluation des scènes s'effectue sur la dimension de « tendance à l'action ». Une troisième étude révèle que l'attention volontaire sur les situations d'intérêt des personnes âgées (positives) et sur les processus d'évaluation modulés par l'âge est nécessaire à l'émergence des effets de positivité. Parallèlement à ces travaux, une méthodologie innovante est proposée pour la classification d'états émotionnels des personnes jeunes et âgées sur la base de leurs signaux électroencéphalographiques. Nous avons obtenu des résultats encourageants qui suggèrent la possibilité cette méthode pour implémenter des interfaces cerveau-machine pour protéger les personnes âgées d'une éventuelle vulnérabilité en raison des effets de positivité. L'ensemble de ces travaux suggèrent que les effets de positivité sont les conséquences de changements sur le plan motivationnel de l'individu âgé, touchant principalement les processus d'évaluation émotionnel. La personne âgée régulerait ses émotions et diminuerait l'impact des émotions négatives lorsque d'autres motivations plus prioritaires sont absentes