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Recently, in order to overcome the disadvantages of unimodal brain-imaging modalities such as low signal-to-noise ratio and vulnerability to motion artifact and to improve system performance, a multimodal imaging system (so-called hybrid system) has been emerging as an attractive alternative. In the present study, to meet the increasing demand on a hybrid brain-imaging data, we introduce open access datasets of electroencephalography (EEG) and near-infrared spectroscopy (NIRS) simultaneously measured during various cognitive tasks. The datasets contain BCI data such as motor imagery (MI)-, and mental arithmetic (MA), and word generation (WG)-related brain signals, and cognitive task data such as n-back (NB)-, and discrimination/selection response (DSR)-related brain signals. We provide the reference results of these datasets, which were validated using analysis pipelines widely used in related research fields. In particular, it was confirmed from classification analysis that a hybrid EEG-NIRS system can yield better classification accuracy than each of unimodal brain-imaging systems.

Current trends and needs in Human-Computer Interaction (HCI) are rapidly increasing; however the possibility to estimate user's state remains unchanged. The need to predict user's state and intentions, and to adapt accordingly in real time to her/his decisions could bring benefits in this dual interaction. Following this purpose, we evaluate the state of a user by means of cognitive processing. In this way, we study the feasibility of detecting the level of cognitive processing, in order to be used for controlling or modifying the interface of a HCI application, such as, an information seeking application. We study this feasibility by analyzing the event-related spectral perturbations evoked by two levels of cognitive processing.

The ability to infer implicit user variables in realtime and in an unobtrusive way would open a broad variety of applications such as adapting the user interface in human-computer interaction or developing safety assistance systems in industrial workplaces. Such information may be extracted from behavior, peripheral physiology and brain activity. Each of these sensors has its advantages and disadvantages suggesting that finally all available features should be fused. While in Brain-Computer Interface (BCI) research powerful methods for the real-time extraction of information from brain signals have been developed, comparatively little effort was spent on the extraction of hidden user states. As a further step in this direction, we propose a novel experimental paradigm to study the feasibility of quantifying how deeply presented information is processed in the brain. An investigation of event-related potentials (ERPs) demonstrates the effectiveness of our task in inducing different levels of cognitive processing and shows which features of brain activity provide discriminative information.

Conventionally, the quality of images and related codecs are assessed using subjective tests, such as Degradation Category Rating. These quality assessments consider the behavioral level only. Recently, it has been proposed to complement this approach by investigating how quality is processed in the brain of a user (using electroencephalography, EEG), potentially leading to results that are less biased by subjective factors. In this paper, a novel method is presented for assessing how image quality is processed on a neural level, using Steady-State Visual Evoked Potentials (SSVEPs) as EEG features. We tested our approach in an EEG study with 16 participants who were presented with distorted images of natural textures. Subsequently, we compared our approach analogously to the standardized Degradation Category Rating quality assessment. Remarkably, our novel method yields a correlation of |r| = 0.93 to MOS on the recorded dataset.

Neuroimaging data are frequently analyzed with multivariate methods. Models expressing the data as a function of underlying factors related to the brain processes under study (signals) are called forward models, while models reversing this functional relationship are called backward models. Weigth vectors of backward models (called extraction filters) indicate the measurement channels informative with respect to isolating the signals. However, being a function of both signal and noise, significant weights may be observed at channels containing pure noise, while a proportion of signal-related channels may be given zero or insignificant weight. In contrast, forward model parameters (activation patterns) may exhibit significant weights only at signal-related channels, and are therefore interpretable with respect to the origin of the brain processes under study. It is sometimes incorrectly assumed that regularization (e.g., sparsification) of backward models makes extraction filters interpretable in the same sense. However, by transforming filters into patterns of corresponding forward models, as outlined here for the linear case, this can be indeed achieved. While these considerations hold for all types of data, the distinction between filters and patterns is particularly crucial for EEG and MEG data: only activation patterns can be localized to brain anatomy using customary inverse methods. We illustrate our theoretical results using a real EEG data example.

Brain Computer Interfaces (BCIs) can be used as communication devices for people affected by neurodegenerative diseases like Amyotrophic Lateral Sclerosis (ALS). The progressive motor-control impairment caused by ALS motivates the development of BCI paradigms that do not rely on eye movement. In the present study, we investigated the feasibility of the first truly gaze-independent visual BCI, called Chroma Speller. Stimuli consist of colors displayed widescreen, which make the focus of gaze irrelevant to the BCI-spelling. The speller was tested online with 9 healthy participants. Mean online symbol-selection accuracy of 88.4% and mean online spelling speed of 1.4 char/min were achieved, using 5 repetitions of the stimuli. These results demonstrate that the abstract association between colors and symbols does not compromise the usability of the speller, leading to performances competitive with the most recent gaze-independent spellers in literature.

The last years have seen a rise in interest in using BCI methodology for investigating non-medical questions beyond the purpose of communication and control. This abstract first provides a short introduction to BCI challenges from a machine learning perspective. The remaining sections present selected applications of BCI discussing in particular the use of EEG in combination with BCI methods for investigating how signal quality is processed on a sensory and cognitive level.

Multimodal techniques have seen a rising interest from the neuroscientific as well as the BCI community in recent times. In this abstract two aspects of multi-modal imaging will be reviewed. Firstly, how recordings of multiple subjects can help in finding subject-independent BCI classifiers and secondly how multi-modal neuroimaging methods, namely combined EEG and NIRS measurements can help in enhancing as well as robustifying BCI performance.

Multimodal recordings of EEG and NIRS of 14 subjects are analyzed in the context of sensory-motor based Brain Computer Interface (BCI). Our findings indicate that performance fluctuations of EEG-based BCI control can be predicted by preceding Near-Infrared Spectroscopy (NIRS) activity. These NIRS-based predictions are then employed to generate new, more robust EEG-based BCI classifiers, which enhance classification significantly, while at the same time minimize performance fluctuations and thus increase the general stability of BCI performance.

One of the foremost goals of rehabilitative Brain-Computer Interface (BCI) research is to design a mental typewriter, that enables paralyzed patients to communicate with their environment based on their brain activity. For the most popular candidate, the so called Matrix Speller, a recent study has shown that the spelling accuracy completely breaks down when the user is not allowed to fixate the target symbol by gaze but rather a central fixation point. This observation raises the need for BCI spellers that work effectively also for patients who have lost the ability to shift their gaze to a required position. Here we will present three variants of visual spellers that are based on covert spatial attention and on non-spatial feature attention. The results of our study with 13 healthy participants impressively demonstrate that these spellers obtain a similar spelling performance during center fixation as the orignal Matrix Speller with target fixation.

Covert attention shifts to the visual periphery induce modulations of α-bandpower over occipital cortex. By demonstrating robust classification of covert attention shifts to four different target locations, a recent magnetoencephalography (MEG) study set the first step for its use as a new input modality to brain-computer interfaces (BCIs) [1]. Here, we set the next step by investigating its feasibility using electroencephalography (EEG). Eight healthy participants had to shift covert visual attention to one of six different target locations while strictly fixating the center of the screen. To enhance the spatial resolution, we used a current source density (CSD) estimate instead of raw voltage maps. Covert attention shifts to the different target locations yielded distinctive topographical distributions of posterior alpha activity.

Brain Computer Interface (BCI) research advanced for more than forty years, providing a rich variety of sophisticated data analysis methods. Yet, most BCI studies have been restricted to the laboratory with controlled and undisturbed environment. BCI research was aiming at developing tools for communication and control. Recently, BCI research has broadened to explore novel applications for improved man-machine interaction. In the present study, we investigated the option to employ neurotechnology in an industrial environment for the psychophysiological optimization of working conditions in such settings. Our findings suggest that it is possible to use BCI-related analysis techniques to qualify responses of an operator by assessing the depth of cognitive processing on the basis of neuronal correlates of behaviourally relevant measures. This could lead to assistive technologies helping to avoid accidents in working environments by designing a collaborative workspace in which the environment takes into account the actual cognitive mental state of the operator.

P300-based Brain Computer Interfaces offer communication pathways which are independent of muscle activity. Mostly visual stimuli, e.g. blinking of different letters are used as a paradigm of interaction. Neural degenerative diseases like amyotrophic lateral sclerosis (ALS) also cause a decrease in sight, but the ability of hearing is usually unaffected. Therefore, the use of the auditory modality might be preferable. This work presents a multiclass BCI paradigm using two-dimensional auditory stimuli: cues are varying in pitch (high/medium/low) and location (left/middle/right). The resulting nine different classes are embedded in a predictive text system, enabling to spell a letter with a 9-class decision. Moreover, an unbalanced subtrial selection is investigated and compared to the well-established sequence-wise paradigm. Twelve healthy subjects participated in an online study to investigate these approaches.

During the last years interest has been growing to find an effective communication channel which translates human intentions into control signals for a computer, the so called brain-computer interface (BCI). One main goal of research is to help patients with severe neuromuscular disabilities by substituting normal motor outputs. Various cortical processes were identified which are suitable for implementing such a system on basis of scalp recorded electroencephalogram (EEG), e.g., slow cortical potentials (SCP) and event-related desynchronisation (ERD) of 10-20 Hz brain rhythms. Until quite recently BCI systems used only few EEG channels but by use of advanced machine learning techniques it became possible to exploit the spatial information provided by multi-channel EEG. While the use of such high density spatial sampling increases the accuracy of the system, it may (depending on the computational effort of the signal processing), pose a problem for the implementation of the feedback in real-time. We propose a method that offers a substantial speed-up for classification of SCP features as used in the Berlin brain computer interface (BBCI). Instead of applying the time consuming low-pass filtering to all, say 120 EEG channels, a suitable spatial projection extracts only 2 or 4 new channels which can be used without any loss of classification accuracy in our experiments. Our approach is based on the technique of common spatial patterns (CSP) which were suggested by Ramoser et al. (2000) to extract ERD features from EEG. While in its original form, CSP is only applicable to oscillatory features, we present a new variant which allows one to use CSP for SCP features without regularisation even in case of large channel numbers or few training samples.

Analyzing biomedical data-e.g. from the brain-we encounter fundamental problems that lie largely in the fields of signal processing and machine learning. The current paper presents at first a method to deal with non-stationary signals, subsequently the signal processing technique of independent component analysis (ICA) is reviewed. We use EEG recordings of continuous auditory perception as illustration for the discussed algorithms.

Cq-profiles are 12-dimensional vectors, each component referring to a pitch class. They can be employed to represent keys. Cq-profiles are calculated with the constant Q filter bank. They have the following advantages: 1) they correspond to probe tone ratings; 2) calculation is possible in real-time; 3) stability is obtained with respect to sound quality; and 4) they are transposable. By using the cq-profile technique as a simple auditory model in combination with the SOM, an arrangement of keys emerges, that resembles results from psychological experiments and from the music theory. Cq-profiles are reliably applied to modulation tracking by introducing a special distance measure.