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Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential component of a BCI system since from the EEG acquisition to the translation of brain activity into meaningful commands, multivariate signal processing algorithms are intensively applied. In this tutorial, the basic BCI concepts, EEG monitoring, BCI operation, the electrophysiological sources of BCI control, future directions, and ambitions are introduced. The main BCI types, namely motor imagery (ERD/ERS), steady state visual evoked potentials (SSVEP), and P300 based BCIs are presented along with practical application examples.The EEG processing for BCI applications is then described in depth. The multivariate nature of the EEG combined with the neuroscience knowledge on hemispheric brain specialization are advantageously taken into account to derive spatial filters (i.e. acrossthe EEG electrodes) to analyze the patterns resulting from motor imagery, visual evoked potentials, and the P300 paradigm.

The ultimate goal of instantaneous blind signal extraction is to find one source out of an instantaneous mixture of many others, without, or with a minimum of, prior information. Extraction can be performed by first identifying the complete mixing system and subsequentlyinverting that system. The goal of this paper is to describe the problems behind blind extraction and to directly find the extracting solution, without first identifying the complete mixing system. The proposed method uses second order statistics to identify the extracting solution and can be applied to mixing problems with different kinds of temporal structure e.g. non-stationarity, coloredness.

In four papers, a set of eleven prerequisites for affective signal processing (ASP) were identified (van den Broek et al., 2010): validation, triangulation, a physiology-driven approach, contributions ofthe signal processing community, identification of users, theoretical specification, integration of biosignals, physical characteristics, historical perspective, temporal construction, and real-world baselines. Additionally, a review (in two parts) of affective computingwas provided. Initiated by the reactions on these four papers, we now present: i) an extension of the review, ii) a post-hoc analysis based on the eleven prerequisites of Picard et al.(2001), and iii) a more detailed discussion and illustrations of temporal aspects with ASP.

Tempo estimation is a subject of intensive research in the field of Music Information Retrieval, as many applications demand the automatic induction of the tempo of musical excerpts. In such applicationsit is desired that a correct tempo estimation would be available to the system at about the same time that the tempo is detected by a human listener. This is technically very difficult because the human listeners are able to use higher level context cues to conduct tempo detection. In fact, many algorithms proposed for tempo detection in the past require a long signal segment for reliable results in tempo estimation. This is clearly a problem in contents such as radio programs, where the rhythmic music content may alternate with, for example, speech segments. There is a wide range of literature methods related to the topic of tempo estimation. So far, tempo estimation systems follow a general scheme that consists of two main steps. In the first step, a feature list is created which is used in the second step in order to detect recurrences of certain events in it. Many different approaches have been proposed in the past for the implementationof the above stages. In this thesis, a new approach to the implementation of the first step is proposed, along with the addition of a final step that will enhance the whole tempo estimation procedure. The proposed method for the extraction of the feature list is based ontransient detection. The term transient is used to describe these points in the time representation of the input signal where abrupt changes take place in its amplitude. The detection is conducted using Gammatone subspace analysis and adaptive Linear Prediction Error Filters. The transient detection function produced from this processing is further processed resulting to the necessary feature list. After the second step, where the feature list is fed as an input to a bank of comb filters resonators, the application of a model that approximates the tempo perception by human listeners is proposed. The later will enhance the results of tempo estimation with perceptual information. The evaluation of the proposed system is done using accuracy measures and musical excerpts obtained from the ISMIR 2004 Tempo Induction Evaluation Exchange benchmark corpus, also used from the first ever attempt to conduct systematic comparison of tempo estimation systems. The results of the evaluation indicate that the proposed method compares favourably with other, state-of-the-art tempo estimation methods, using only one frame of the musical excerpts when most of the literature methods demand the processing of the whole piece.

Pulse oximeters measure a patient’s heart rate and blood oxygenationby illuminating the skin and measuring the intensity of the light that has propagated through it. The measured intensities, called photoplethysmograms (PPGs), are highly susceptible to motion, which candistort the PPG derived data. Part of the motion artifacts are considered to result from sensor deformation, leading to a change in emitter-detector distance. It is hypothesized that these motion artifacts correlate to movement of the emitter with respect to the skin. This has been investigated in a laboratory setup in which motion artifacts can be reproducibly generated by translating the emitter with respect to a flowcell that models skin perfusion. The top of the flowcell is a diffuse scattering Delrin skin phantom under which a cardiac induced blood pulse is modeled by a changing milk volume. By illuminating the flowcell, a PPG can be measured. The emitter’s translation has been accurately measured using self-mixing interferometry (SMI). The motion artifacts in the PPG as a result of emitter motion are shown to correlate with the emitter’s displacement. Moreover, it is shown that these artifacts are significantly reduced by a least-mean-square algorithm that uses the emitter’s displacement measured via SMI as artifact reference.

Currently, photoplethysmograms (PPGs) are mostly used to determine apatient's blood oxygenation and pulse rate. However, PPG morphologyconveys more information about the patient's cardiovascular status.Extracting this information requires measuring clean PPG waveformsthat are free of artifacts. PPGs are highly susceptible to motion, which can distort the PPG derived data. Part of the motion artifactsare considered to result from sensor-tissue motion and sensor deformation. It is hypothesized that these motion artifacts correlate withmovement of the sensor with respect to the skin. This hypothesis has been proven true in a laboratory setup. In-vitro PPGs have been measured in a skin perfusion phantom that is illuminated by a laser diode. Optical motion artifacts are generated in the PPG by translating the laser diode with respect to the PPG photodiode. The optical motion artifacts have been reduced significantly in-vitro, by using anormalized least-mean-square algorithm with only a single coefficient that uses the laser's displacement as a reference for the motion artifacts. Laser displacement has been measured accurately via self-mixing interferometry by a compact laser diode with a ball lens integrated into the package, which can be easily integrated into a commercial sensor.