Prediction of difficulty levels in video games from ongoing EEG

Real-time assessment of mental workload from EEG plays an important role in enhancing symbiotic interaction of human operators in immersive environments. In this study we thus aimed at predicting the difficulty level of a video game a person is playing at a particular moment from the ongoing EEG activity. Therefore, we made use of power modulations in the theta (4–7 Hz) and alpha (8–13 Hz) frequency bands of the EEG which are known to reflect cognitive workload. Since the goal was to predict from multiple difficulty levels, established binary classification approaches are futile. Here, we employ a novel spatial filtering method (SPoC) that finds spatial filters such that their corresponding bandpower dynamics maximally covary with a given target variable, in this case the difficulty level. EEG was recorded from 6 participants playing a modified Tetris game at 10 different difficulty levels. We found that our approach predicted the levels with high accuracy, yielding a mean prediction error of less than one level.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

Wyrm: A Brain-Computer Interface Toolbox in Python

In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm (https://github.com/bbci/wyrm), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm’s software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine InteraktionBMBF, 16SV5839, Maschinelles Lernen zur Optimierung der Kommunikation schwerstgelähmter Patienten per BC

User-centered design in brain–computer interfaces — a case study

The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found

Unsupervised classification of operator workload from brain signals

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

Involving Motor Capabilities in the Formation of Sensory Space Representations

A goal of sensory coding is to capture features of sensory input that are behaviorally relevant. Therefore, a generic principle of sensory coding should take into account the motor capabilities of an agent. Up to now, unsupervised learning of sensory representations with respect to generic coding principles has been limited to passively received sensory input. Here we propose an algorithm that reorganizes an agent's representation of sensory space by maximizing the predictability of sensory state transitions given a motor action. We applied the algorithm to the sensory spaces of a number of simple, simulated agents with different motor parameters, moving in two-dimensional mazes. We find that the optimization algorithm generates compact, isotropic representations of space, comparable to hippocampal place fields. As expected, the size and spatial distribution of these place fields-like representations adapt to the motor parameters of the agent as well as to its environment. The representations prove to be well suited as a basis for path planning and navigation. They not only possess a high degree of state-transition predictability, but also are temporally stable. We conclude that the coding principle of predictability is a promising candidate for understanding place field formation as the result of sensorimotor reorganization