Invited Lectures
Invited Lecture:
Prof. Thomas Penzel, Charite - Universitätsmedizin Berlin

Signal recording and non-linear processing in sleep research
Sleep disorders are found to be more prevalent than previously realized. This may be a consequence of a modern society which optimizes work and social activities up to the edge. In order to investigate normal and disturbed sleep, we record biosignals both in the sleep laboratory and at home. Signals may be recorded directly, such as EEG, EOG, EMG from the head of the sleeping person, or indirectly, such as ECG, heart rate, respiration, pulse wave. Signals may be recorded with little contact or no contact systems such as actigraphy, body movement, bed sensors or bedside radiofrequency sensors. Some signals are new in sleep research and require new technology and analysis concepts. Always biosignals were recorded with an appropriate time and amplitude resolution, and then we derive physiological functions. We can identify wakefulness and sleep, we can derive details about sleep, such as light sleep, deep sleep, and REM sleep, arousals and sleep fragmentation. Not only classical methods in the time and frequency domain are used, but also more recent methods using statistical approaches are applied. This allows recognizing normal and restorative sleep and identifying sleep disorders as well. Some sleep disorders imply cardiovascular consequences and require treatment. Sleep disordered breathing is the disorder with most cardiovascular consequences. Many diagnostic tools focus on this group of disorders [1]. Diagnostic methods and perspectives are presented in this communication.

[1] IEEE Engineering in Biology and Medicine Society: ``The Science of Sleep". Pulse Magazine. Sept./Oct. issue 2014.

Invited Lecture:
Prof. Patrice Abry, Ecole Normale Supérieure de Lyon

Intrapartum Fetal Heart Rate Analysis:
From fractal features to sparse feature-selection based Classification
Authors: Patrice ABRY, CNRS, Physics Lab., ENS Lyon, France
(joint work with: M. Doret, Women-Mother-Child Hospital, Lyon, France,
J. Spilka, V. Chudacek, CIIRC, Czech Technical University in Prague, Czech Republic,
N. Pustelnik, R. Leonarduzzi, Physics Lab., ENS Lyon, France)

Fetal Heart Rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. The present work renews FHR analysis and fetal acidosis detection in two ways. First, fractal based features are shown to constitute relevant tools for the assessment of cardiac variability, that significantly outperform and thus satisfactory replace other traditional assessment of cardiac variability such as LF/HF ratio, that relies either on the splitting into a priori chosen frequency bands of the spectral contant of data, or on spectral and DFA based scaling exponents. Second, fetal acidosis detection is commonly formulated as a pH based classification problem. Our original proposition is to promote Sparse Support Vector Machine classification that permits to select a small number of relevant features as well as to achieve efficient fetal acidosis detection. Concepts and tools are illustrated at on a large (1288 subjects) and well documented database, collected at french public academic Hospital in Lyon. It is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0.73 and specificity 0.75, outperforming clinical practice). The subset of selected features receive simple interpretation in clinical practice. A second large database collected in Czech Republic is further used to show the generalization ability of both fractal features and Sparse Support Vector Machine classification.

Invited Lecture:
Prof. Fabio Babiloni, University of Rome Sapienza

Brain computer interfaces for the industrial application of cognitive neuroscience
Are the cognitive neuroscience ready to be used in advanced industrial contexts? In this presentation it will be depicted a possible path for the use of advanced findings in cognitive neuroscience by using the electroencephalogram not only in the medical environment (e.g. to improve the limbfs rehabilitation path for patients affected by stroke). In particular, applications of advanced EEG signal processing technique will be illustrated in the marketing context (neuromarketing) as well as in the aerospace-aeronautic environment, through the on-line monitoring of the mental workload of pilots, air traffic controllers and other category of professional drivers during their actual operations. Four main areas will be described:
1) Brain Computer Interface. In this part of the talk different applications of the brain computer interfaces (BCI) technology will be first presented. All the applications will be obtained by using the computerized analysis of the electrical activity of the human brain, gathered by a net of electrodes placed on the scalp surface (electroencephalogram, EEG). It will be described how by using the voluntary modulation of EEG activity normal subjects could control external devices such as a cursor on the screen, a mobile robot as well as a wheelchair. Successively, it will be illustrated how the BCI technology could be inserted within the rehabilitation path of the patients suffered of brain strokes. In particular, it will be showed how BCI technologies could enhance the rehabilitation exercise, by including the presentation of the attempt of the movement as early as possible to the patients, although they were not yet able to move their limbs. However, BCI applications could be extended beyond the use in the clinical context, and application in the area of the gsynthetic telepathyh are already investigated by DARPA and by the present research group.
2) Neuromarketing: Application of neuroscience in the evaluation of relevant marketing stimuli will be described. The main cognitive neuroscience indicators for the appreciation of an audiovisual sensory stimuli (e.g. a TV commercials) will be also described. The use of such EEG-based indicators in practical situation will be also illustrated.
3) Online detection of mental workload: Successively, it will be showed different applications of the collection of brain activity in working contexts related to the airplanes pilots. It will be described as it is possible to detect the brain activity related to the insurgence of mental workload. It will be speculated that such detection could be employed in a short future to generate devices able to warn the operators about their perceived workload. Example of such detection of mental workload will be presented in three different conditions: on civil airline pilots, on military pilots and on car drivers.
4) Neuroaestethic: The issue of how we perceive the beauty will be also addressed by the talk, through the presentation of main results obtained by monitoring the EEG activity during the visit of two art galleries with the pictures of Tiziano Vecellio and Jan Veermer.
The possibility to detect in a reliable way the cerebral activity during "real-life" conditions and the possibility to detect brain activity with dry electrodes will be also discussed. Quoting the scientist Martha Farah, the issue is not "if" but "when" the neuroscience will shape our future. We are thinking that such time is arrived.