Wearable electroencephalogram (EEG) solutions allow portability and real-time measurements in uncontrolled conditions. For reliable and reproducible interpretation of the EEG data, it is essential to accurately identify EEG segments contaminated by artefacts. Two data quality indicator approaches are proposed: pragmatic and regression based. The former extracts statistical features and applies data-driven thresholding, while the latter uses a regression model on the same set of statistical features to predict data quality. The performance of the approaches is validated against EEG data recorded during uncontrolled laboratory and free-living conditions, and compared to a validated approach. The proposed approaches achieve average accuracy of over [Formula: see text] in detecting artefactual data, which is higher than the FORCe signal quality estimation method ([Formula: see text]). The main strength of the proposed algorithms is in the significant increase of specificity over the state-of-the-art. The two models perform equally across different databases. Training of the two approaches on free-living conditions data showed better generalization when tested on different types of databases, i.e., uncontrolled laboratory and free-living. Although the accuracy in determining artefact-contaminated data is highest when using a window size of 8 s, the accuracy drop is minor when using shorter window size, demonstrating another advantage over existing methods. Given low complexity of both pragmatic and regression approach, it facilitates a real-time implementation, which is demonstrated using a wearable EEG headset system available at IMEC.

Characterizing cognitive states of a person based on surface electroencephalography (EEG) is traditionally performed by analyzing spectral power of the electrical brain activity at different frequency bands. Although frequency band activity over different electrode locations can provide valuable input for discriminating conscious and unconscious or drowsy and fully awaken brain states, it fails when fine grained cognitive state and effort estimation is required. This paper demonstrates that nonlinear features can be used to describe aspects of cognitive effort. Recurrence quantification analysis (RQA) features were used to discriminate mental relaxation, math calculations, and contemplating scientific articles. However, only when used in combination with traditional spectral features, the discrimination performance increases. The most dominant RQA features are recurrence rate and ratio, while the most dominant spectral features are relative beta and delta power.

Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.

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A unified database framework that will enable better comprehension of ranked XML retrieval is still a challenge in the XML database field. We propose a logical algebra, named score region algebra, that enables transparent specification of information retrieval (IR) models for XML databases. The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML retrieval: element relevance score computation, element score propagation, and element score combination. To illustrate the usefulness of our algebra we instantiate four different, well known IR scoring models, and combine them with different score propagation and combination functions. We implemented the algebra operators in a prototype system on top of a low-level database kernel. The evaluation of the system is performed on a collection of IEEE articles in XML format provided by INEX. We argue that state of the art XML IR models can be transparently implemented using our score region algebra framework on top of any low-level physical database engine or existing RDBMS, allowing a more systematic investigation of retrieval model behavior.

In this paper, we focus on the use of three different techniques that support automatic derivation of video content from raw video data, namely, a spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. These techniques are validated in the particular domain of tennis and Formula 1 race videos. We present the experimental results for the detection of events such as net-playing, rally, service, and forehand stroke among others in the Tennis domain, as well as excited speech, start, fly-out, passing, and highlights in the Formula 1 domain.

As amounts of publicly available video data grow, the need to automatically infer semantics from raw video data becomes significant. In this paper, we focus on the use of dynamic Bayesian networks (DBN) for that purpose, and demonstrate how they can be effectively applied for fusing the evidence obtained from different media information sources. The approach is validated in the particular domain of Formula I race videos. For that specific domain we introduce a robust audiovisual feature extraction scheme and a text recognition and detection method. Based on numerous experiments performed with DBN, we give some recommendations with respect to the modeling of temporal and atemporal dependences within the network. Finally, we present the experimental results for the detection of excited speech and the extraction of highlights, as well as the advantageous query capabilities of our system.