Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

The power of feedback.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

/pdf/a-review-of-classification-algorithms-for-eeg-based-brain-p1h22hb1wr.pdf

A review of classification algorithms for EEG-based brain–computer interfaces

The conflict between vergence (eye movement) and accommodation (crystalline lens deformation) occurs in every stereoscopic display. It could cause important stress outside the "zone of comfort", when stereoscopic effect is too strong. This conflict has already been studied using questionnaires, during viewing sessions of several minutes. The present pilot study describes an experimental protocol which compares two different comfort conditions using electroencephalography (EEG) over short viewing sequences. Analyses showed significant differences both in event-related potentials (ERP) and in frequency bands power. An uncomfortable stereoscopy correlates with a weaker negative component and a delayed positive component in ERP. It also induces a power decrease in the alpha band and increases in theta and beta bands. With fast responses to stimuli, EEG is likely to enable the conception of adaptive systems, which could tune the stereoscopic experience according to each viewer.

https://hal.inria.fr/hal-00982782/document

Assessing the zone of comfort in stereoscopic displays using EEG

Estimating mental workload from brain signals such as Electroencephalography (EEG) has proven very promising in multiple Human-Computer Interaction (HCI) applications, e.g., to design games or educational applications with adaptive difficulty, or to assess how cognitively difficult to use an interface can be. However, current EEG-based workload estimation may not be robust enough for some practical applications. Indeed, the currently obtained workload classification accuracies are relatively low, making the resulting estimations not fully trustable. This paper thus studies promising modern machine learning algorithms, including Riemannian geometry-based methods and Deep Learning, to estimate workload from EEG signals. We study them with both user-specific and user-independent calibration, to go towards calibration-free systems. Our results suggested that a shallow Convolutional Neural Network obtained the best performance in both conditions, outperforming state-of-the-art methods on the used data sets. This suggests that Deep Learning can bring new possibilities in HCI.

https://hal.inria.fr/hal-01849055/document

Towards Robust Neuroadaptive HCI: Exploring Modern Machine Learning Methods to Estimate Mental Workload From EEG Signals

In this paper we explore the use of electrical biosignals measured on scalp and corresponding to mental relaxation and concentration tasks in order to control an object in a video game. To evaluate the requirements of such a system in terms of sensors and signal processing we compare two designs. The first one uses only one scalp electroencephalographic (EEG) electrode and the power in the alpha frequency band. The second one uses sixteen scalp EEG electrodes and machine-learning methods. The role of muscular activity is also evaluated using five electrodes positioned on the face and the neck. Results show that the first design enabled 70% of the participants to successfully control the game, whereas 100% of the participants managed to do it with the second design based on machine learning. Subjective questionnaires confirm these results: users globally felt to have control in both designs, with an increased feeling of control in the second one. Offline analysis of face and neck muscle activity shows that this activity could also be used to distinguish between relaxation and concentration tasks. Results suggest that the combination of muscular and brain activity could improve performance of this kind of system. They also suggest that muscular activity has probably been recorded by EEG electrodes.

/pdf/using-scalp-electrical-biosignals-to-control-an-object-by-423k32gb7d.pdf

Using scalp electrical biosignals to control an object by concentration and relaxation tasks: Design and evaluation

Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns into messages or commands for an interactive application. As such the technology used to design them, and in particular to design passive BCIs which are a new means to perform mental state monitoring, can greatly beneﬁt the neuroergonomics ﬁeld. Therefore, this chapter describes the classical structure of the brain signal processing chain employed in BCIs, notably presenting the typically used preprocessing (spatial and spectral ﬁltering, artefact removal), feature extraction and classiﬁcation algorithms. It also gives examples of the use
of BCI technology for neuroergonomics applications, either oﬄine for evaluation purposes (e.g. cockpit design or stereoscopic displays’ assessment), or online for adaptation purposes (e.g. video game diﬃculty level or air traffic controller display adaptation).

/pdf/brain-computer-interface-contributions-to-neuroergonomics-om4qr00l8m.pdf

Brain–Computer Interface Contributions to Neuroergonomics

In this paper, we introduce Waveform Length (WL), a new feature for ElectroEncephaloGraphy (EEG) signal classification which measures the signal complexity. We also propose the Waveformlength Optimal Spatial Filter (WOSF), an optimal spatial filter to classify EEG signals based on WL features. Evaluations on 15 subjects suggested that WOSF with WL features provide performances that are competitive with that of Common Spatial Patterns (CSP) with Band Power (BP) features, CSP being the optimal spatial filter for BP features. More interestingly, our results suggested that combining WOSF with CSP features leads to classification performances that are significantly better than that of CSP alone (80% versus 77% average accuracy respectively).

https://hal.inria.fr/hal-00717617/document

Fabien Lotte

Papers

Assessing the zone of comfort in stereoscopic displays using EEG

Towards Robust Neuroadaptive HCI: Exploring Modern Machine Learning Methods to Estimate Mental Workload From EEG Signals

Using scalp electrical biosignals to control an object by concentration and relaxation tasks: Design and evaluation

Brain–Computer Interface Contributions to Neuroergonomics

A new feature and associated optimal spatial filter for EEG signal classification: Waveform Length