Publications

Manual processing of sleep recordings is extremely time-consuming. Efforts to automate this process have shown promising results, but automatic systems are generally evaluated on private databases, not allowing accurate cross-validation with other systems. In lacking a common benchmark, the relative performances of different systems are not compared easily and advances are compromised. To address this fundamental methodological impediment to sleep study, we propose an open-access database of polysomnographic biosignals. To build this database, whole-night recordings from 200 participants [97 males (aged 42.9 ± 19.8 years) and 103 females (aged 38.3 ± 18.9 years); age range: 18-76 years] were pooled from eight different research protocols performed in three different hospital-based sleep laboratories. All recordings feature a sampling frequency of 256 Hz and an electroencephalography (EEG) montage of 4-20 channels plus standard electro-oculography (EOG), electromyography (EMG), electrocardiography (ECG) and respiratory signals. Access to the database can be obtained through the Montreal Archive of Sleep Studies (MASS) website (http://www.ceams-carsm.ca/en/MASS), and requires only affiliation with a research institution and prior approval by the applicant's local ethical review board. Providing the research community with access to this free and open sleep database is expected to facilitate the development and cross-validation of sleep analysis automation systems. It is also expected that such a shared resource will be a catalyst for cross-centre collaborations on difficult topics such as improving inter-rater agreement on sleep stage scoring.

This article presents an exploratory study investigating the possibility of predicting the time occurrence of a motor event related potential (ERP) from a kinematic analysis of human movements. Although the response-locked motor potential may link the ERP components to the recorded response, to our knowledge no previous attempt has been made to predict a priori (i.e. before any contact with the electroencephalographic data) the time occurrence of an ERP component based only on the modeling of an overt response. The proposed analysis relies on the delta-lognormal modeling of velocity, as proposed by the kinematic theory of rapid human movement used in several studies of motor control. Although some methodological aspects of this technique still need to be fine-tuned, the initial results showed that the model-based kinematic analysis allowed the prediction of the time occurrence of a motor command ERP in most participants in the experiment. The average map of the motor command ERPs showed that this signal was stronger in electrodes close to the contra-lateral motor area (Fz, FCz, FC1, and FC3). These results seem to support the claims made by the kinematic theory that a motor command is emitted at time t(0), the time reference parameter of the model. This article proposes a new time marker directly associated with a cerebral event (i.e. the emission of a motor command) that can be used for the development of new data analysis methodologies and for the elaboration of new experimental protocols based on ERP.

The generation of handwriting is a complex neuromotor skill requiring the interaction of many cognitive processes. It aims at producing a message to be imprinted as an ink trace left on a writing medium. The generated trajectory of the pen tip is made up of strokes superimposed over time. The Kinematic Theory of rapid human movements and its family of lognormal models provide analytical representations of these strokes, often considered as the basic unit of handwriting. This paradigm has not only been experimentally confirmed in numerous predictive and physiologically significant tests but it has also been shown to be the ideal mathematical description for the impulse response of a neuromuscular system. This latter demonstration suggests that the lognormality of the velocity patterns can be interpreted as reflecting the behavior of subjects who are in perfect control of their movements. To illustrate this interpretation, we present a short overview of the main concepts behind the Kinematic Theory and briefly describe how its models can be exploited, using various software tools, to investigate these ideal lognormal behaviors. We emphasize that the parameters extracted during various tasks can be used to analyze some underlying processes associated with their realization. To investigate the operational convergence hypothesis, we report on two original studies. First, we focus on the early steps of the motor learning process as seen as a converging behavior toward the production of more precise lognormal patterns as young children practicing handwriting start to become more fluent writers. Second, we illustrate how aging affects handwriting by pointing out the increasing departure from the ideal lognormal behavior as the control of the fine motricity begins to decline. Overall, the paper highlights this developmental process of merging toward a lognormal behavior with learning, mastering this behavior to succeed in performing a given task, and then gradually deviating from it with aging.

This paper reports the results of a model-based analysis of movements gathered in a 4×4 experimental design of speed/accuracy tradeoffs with variable target distances and width. Our study was performed on a large (120 participants) and varied sample (both genders, wide age range, various health conditions). The delta-lognormal equation was used for data modeling to investigate the interaction between the output of the agonist and the antagonist neuromuscular systems. Empirical observations show that the subjects must correlate more tightly the impulse commands sent to both neuromuscular systems in order to achieve good performances as the difficulty of the task increases whereas the correlation in the timing of the neuromuscular action co-varies with the size of the geometrical properties of the task. These new phenomena are discussed under the paradigm provided by the Kinematic Theory and new research hypotheses are proposed for further investigation of the speed/accuracy tradeoffs.

Fast reaching movements are an important component of our daily interaction with the world and are consequently under investigation in many fields of science and engineering. Today, useful models are available for such studies, with tools for solving the inverse dynamics problem involved by these analyses. These tools generally provide a set of model parameters that allows an accurate and locally optimal reconstruction of the original movements. Although the solutions that they generate may provide a data curve fitting that is sufficient for some pattern recognition applications, the best possible solution is often necessary in others, particularly those involving neuroscience and biomedical signal processing. To generate these solutions, we present a globally optimal parameter extractor for the delta-lognormal modeling of reaching movements based on the branch-and-bound strategy. This algorithm is used to test the impact of white noise on the delta-lognormal modeling of reaching movements and to benchmark the state-of-the-art locally optimal algorithm. Our study shows that, even with globally optimal solutions, parameter averaging is important for obtaining reliable figures. It concludes that physiologically derived rules are necessary, in addition to global optimality, to achieve meaningful ∆Λ extractions which can be used to investigate the control patterns of these movement primitives.

In the context of the occidental population aging, new preventive approaches must be developed to reduce the disability related to brain stroke in the elderly. The stroke susceptibility assessment based on the analysis of human movements is one of the potential avenues needing investigation. As a first step in this direction, this paper reports results on the relationship linking the most important stroke risk factors to some characteristics of human movement. Various features were extracted using the Sigma-Lognormal model on 1440 stereotypical triangular movements performed by 120 subjects having different health conditions. These features were combined through a linear modeling to maximize the predictability of presence of stroke risk factors in the studied cohort. The receiver operating characteristic (ROC) curve and the area under this curve (AUC) were used to evaluate the clinical significance of this relationship. Using only the information derived from the movements, the six tested risk factors (cardiac problems, diabetes mellitus, hypercholesterolemia, hypertension, obesity, and cigarette smoking) can be predicted with an AUC ranging from .68 to .82.

The main goal of this work is to determine whether a computer mouse can be used as a low-cost device for the acquisition of two-dimensional human movement velocity signals in the context of psychophysical studies and biomedical applications. A comprehensive overview of the related literature is presented, and the problem of characterizing mouse movement acquisition is analyzed and discussed. Then, the quality of velocity signals acquired with this kind of device is measured on horizontal oscillatory movements by comparing the mouse data to the signals acquired simultaneously by a video motion tracking system and a digitizing tablet. A synthesis of the information gathered in this work indicates that the computer mouse can be used for the reliable acquisition of biosignals in the context of human movement studies, particularly for many applications dealing with the velocity of the end effector of the upper limb. This paper concludes by discussing the possibilities and limitations of such use.

In this paper, 14 healthy subjects in two age groups have produced rapid handwriting strokes with a direction reversal. The delta-lognormal model was used to obtain a detailed description of the velocity of these movements and of the neuromuscular synergy that produces them. This modeling also allowed the derivation of new hypothesis on the nature of the slowing effect due to aging (i.e., a direct effect or a coping strategy) and on its repartition on the different steps of the movement production (i.e., its preparation versus its execution). Our analysis revealed a substantial increase of neuromuscular response delays and a decrease of the command amplitudes with age. For the parameters that show a significant decrease in performance, the agonist and antagonist systems were affected similarly. In addition, we observed that the age has a proportional effect on the various time characteristics of the movements and that even in the case of a significant slowing down of the neuromuscular systems, the elderly can still achieve optimal movement responses, characterized by the use of a single delta-lognormal primitive. This performance might be related to the preservation of some movement timing properties and relationships between the agonist and the antagonist neuromuscular systems.

In this paper, the existence of oscillations for a class of recurrent neural networks with time delays between neural interconnections is investigated. By using the fixed point theory and Liapunov functional, we prove that a recurrent neural network might have a unique equilibrium point which is unstable. This particular type of instability, combined with the boundedness of the solutions of the system, will force the network to generate a permanent oscillation. Some necessary and sufficient conditions for these oscillations are obtained. Simple and practical criteria for fixing the range of parameters in this network are also derived. Typical simulation examples are presented.