One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper.

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Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms

Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Feature Extraction and Selection for Emotion Recognition from EEG

Elements of Information Theory (2nd ed.). Thomas M. Cover and Joy A. Thomas

For generations, humans have fantasized about the ability to create devices that can see into a persons mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical processes within the brain which correspond with certain forms of thought. Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction broadly surveys research in the Brain-Computer Interface domain. More specifically, each chapter articulates some of the challenges and opportunities for using brain sensing in Human-Computer Interaction work, as well as applying Human-Computer Interaction solutions to brain sensing work. For researchers with little or no expertise in neuroscience or brain sensing, the book provides background information to equip them to not only appreciate the state-of-the-art, but also ideally to engage in novel research. For expert Brain-Computer Interface researchers, the book introduces ideas that can help in the quest to interpret intentional brain control and develop the ultimate input device. It challenges researchers to further explore passive brain sensing to evaluate interfaces and feed into adaptive computing systems. Most importantly, the book will connect multiple communities allowing research to leverage their work and expertise and blaze into the future.

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Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction

Over the past several decades, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, building robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual and auditory brain responses remains a challenging problem. In this paper, we review the current state and future challenges of visual and auditory BCI systems. First, we describe a new taxonomy based on the multiple access methods used in telecommunication systems. Then, we discuss the challenges of translating current technology into real-life practices and outline potential avenues to address them. Specifically, this review aims to provide useful guidelines for exploring new paradigms and methodologies to improve the current visual and auditory BCI technology.

https://sccn.ucsd.edu/~yijun/pdfs/TBME14.pdf

Visual and Auditory Brain–Computer Interfaces

In recent years we have seen a rising interest in brain-computer interfacing for human-computer interaction and potential game applications. Until now, however, we have almost only seen attempts where BCI is used to measure the affective state of the user or in neurofeeedback games. There have hardly been any attempts to design BCI games where BCI is considered to be one of the possible input modalities that can be used to control the game. One reason may be that research still follows the paradigms of the traditional, medically oriented, BCI approaches. In this paper we discuss current BCI research from the viewpoint of games and game design. It is hoped that this survey will make clear that we need to design different games than we used to, but that such games can nevertheless be interesting and exciting.

/pdf/turning-shortcomings-into-challenges-brain-computer-2urf8mmkey.pdf

Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. Therefore, this chapter gives an overview of the state of the art of BCI in games and discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.

Brain-Computer Interfacing and Games

In this paper, we describe our investigation of traces of naturally occurring emotions in electrical brain signals, that can be used to build interfaces that respond to our emotional state. This study confirms a number of known affective correlates in a realistic, uncontrolled environment for the emotions of valence (or pleasure), arousal and dominance: (1) a significant decrease in frontal power in the theta range is found for increasingly positive valence, (2) a significant frontal increase in power in the alpha range is associated with increasing emotional arousal, (3) a significant right posterior power increase in the delta range correlates with increasing arousal and (4) asymmetry in power in the lower alpha bands correlates with self-reported valence. Furthermore, asymmetry in the higher alpha bands correlates with self-reported dominance. These last two effects provide a simple measure for subjective feelings of pleasure and feelings of control.

Valence, arousal and dominance in the EEG during game play

The P300 event-related potential (ERP) can be used to infer whether an observer is looking at a target or not. Common practice in P300 experiments and applications is that observers are asked to fixate their eyes while stimuli are presented. We investigated the possibility to differentiate between single target and nontarget fixations in a target search task involving eye movements by using EEG epochs synchronized to fixation onset (fixation-related potentials: FRPs). Participants systematically scanned search displays consisting of six small Landolt Cs in search of Cs with a particular orientation. After each search display, they indicated whether and where target Cs had been presented. As expected, an FRP component consistent with the P300 reliably distinguished between target and nontarget fixations. It was possible to classify single FRPs into target and nontarget FRPs above chance (on average 62% correct, where 50% would be chance). These results are the first step to practical applications such as covertly monitoring observers' interests and supporting search tasks.

/pdf/distinguishing-between-target-and-nontarget-fixations-in-a-3y24pmn556.pdf

Boris Reuderink

Papers

Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

Brain-Computer Interfacing and Games

Valence, arousal and dominance in the EEG during game play

Distinguishing between target and nontarget fixations in a visual search task using fixation-related potentials