Marijn van Vliet

Single-Trial ERP Component Analysis Using a Spatiotemporal LCMV Beamformer

Goal: For statistical analysis of event-related potentials (ERPs), there are convincing arguments against averaging across stimuli or subjects. Multivariate filters can be used to isolate an ERP component of interest without the averaging procedure. However, we would like to have certainty that the output of the filter accurately represents the component. Methods: We extended the linearly constrained minimum variance (LCMV) beamformer, which is traditionally used as a spatial filter for source localization, to be a flexible spatiotemporal filter for estimating the amplitude of ERP components in sensor space. In a comparison study on both simulated and real data, we demonstrated the strengths and weaknesses of the beamformer as well as a range of supervised learning approaches. Results: In the context of measuring the amplitude of a specific ERP component on a single-trial basis, we found that the spatiotemporal LCMV beamformer is a filter that accurately captures the component of interest, even in the presence of both structured noise (e.g., other overlapping ERP components) and unstructured noise (e.g., ongoing brain activity and sensor noise). Conclusion: The spatiotemporal LCMV beamformer method provides an accurate and intuitive way to conduct analysis of a known ERP component, without averaging across trials or subjects. Significance: Eliminating averaging allows us to test more detailed hypotheses and apply more powerful statistical models. For example, it allows the usage of multilevel regression models that can incorporate between subject/stimulus variation as random effects, test multiple effects simultaneously, and control confounding effects by partial regression.

Steady State Visual Evoked Potential Based Computer Gaming – The Maze

We introduce a game, called “The Maze”, as a brain-computer interface (BCI) application in which an avatar is navigated through a maze by analyzing the player’s steady-state visual evoked potential (SSVEP) responses recorded with electroencephalography (EEG). The same computer screen is used for displaying the game environment and for the visual stimulation. The algorithms for EEG data processing and SSVEP detection are discussed in depth. We propose the system parameter values, which provide an acceptable trade-off between the game control accuracy and interactivity.

Response-Related Potentials during Semantic Priming: The Effect of a Speeded Button Response Task on ERPs

This study examines the influence of a button response task on the event-related potential (ERP) in a semantic priming experiment. Of particular interest is the N400 component. In many semantic priming studies, subjects are asked to respond to a stimulus as fast and accurately as possible by pressing a button. Response time (RT) is recorded in parallel with an electroencephalogram (EEG) for ERP analysis. In this case, the response occurs in the time window used for ERP analysis and response-related components may overlap with stimulus-locked ones such as the N400. This has led to a recommendation against such a design, although the issue has not been explored in depth. Since studies keep being published that disregard this issue, a more detailed examination of influence of response-related potentials on the ERP is needed. Two experiments were performed in which subjects pressed one of two buttons with their dominant hand in response to word-pairs with varying association strength (AS), indicating a personal judgement of association between the two words. In the first experiment, subjects were instructed to respond as fast and accurately as possible. In the second experiment, subjects delayed their button response to enforce a one second interval between the onset of the target word and the button response. Results show that in the first experiment a P3 component and motor-related potentials (MRPs) overlap with the N400 component, which can cause a misinterpretation of the latter. In order to study the N400 component, the button response should be delayed to avoid contamination of the ERP with response-related components.

Looking around with your brain in a virtual world

Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This real-time system and a game application in which these pipelines are to be used were implemented. The game is set in a virtual environment where player is a wildlife photographer on an uninhabited island. Overt attention is used to adjust the angle of the first person camera, when the player is tracking animals. When making a photograph, the animal will flee when it notices it is looked at directly, so covert attention is required to get a good shot. Future work will entail user tests with this system to evaluate usability, user experience, and characteristics of the signals related to overt and covert attention when used in such an immersive environment.

Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network

In this paper, we report on the decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings by means of a multilayer feedforward neural network based on multivalued neurons. Networks of this kind have inputs and outputs which are well fitted for the considered task. The dependency of the decoding accuracy w.r.t. the number of targets and the decoding window size is discussed. Comparing existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for the larger amount of target classes and the sufficient size of decoding window. The necessity of the proper frequency selection for each subject is discussed.

Subject-adaptive steady-state visual evoked potential detection for brain-computer interface

We report on the development of a four command Brain-Computer Interface (BCI) based on steady-state visual evoked potential (SSVEP) responses detected from human electroencephalograms (EEGs). The proposed system combines spatial filtering, feature extraction and selection, and a classifier. Two types of classifiers were compared: one based on equal treatment of all harmonics in all EEG channels and the second based on preliminary training resulting in a weighted treatment of the harmonics. Results from six healthy subjects are evaluated.

Decoding phase-based information from SSVEP recordings: A comparative study

In this paper, we report on the decoding of phase-based information, from steady-state visual evoked potential (SSVEP) recordings, by means of different classifiers. In addition to the ones reported in the literature, we also consider other types of classifiers such as the multilayer feedforward neural network based on multi-valued neurons (MLMVN), and the classifier based on fuzzy logic, which we especially tuned for phase-based SSVEP decoding. The dependency of the decoding accuracy on the number of targets and on the decoding window size are discussed. When comparing existing phase-based SSVEP decoding methods with the proposed ones, we are able to show that the latter ones perform better, for different parameter settings, but especially when having multiple targets. The necessity of optimizing the target frequencies to the individual subject is also discussed.

Brain-computer interface research at Katholieke Universiteit Leuven

We present an overview of our Brain-computer interface (BCI) research, invasive as well as non-invasive, during the past four years. The invasive BCIs are based on local field-and action potentials recorded with microelectrode arrays implanted in the visual cortex of the macaque monkey. The non-invasive BCIs are based on electroencephalogram (EEG) recorded from a human subjects scalp. Several EEG paradigms were used to enable the subject to type text or to select icons on a computer screen, without having to rely on ones fingers, gestures, or any other form of motor activity: the P300 event-related potential, the steady-state visual evoked potential, and the error related potential. We report on the status of our EEG BCI tests on healthy subjects as well as patients with severe communication disabilities, and our demonstrations to a broad audience to raise the public awareness of BCI.

Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials

Modern multivariate methods have enabled the application of unsupervised techniques to analyze neurophysiological data without strict adherence to predefined experimental conditions. We demonstrate a multivariate method that leverages priming effects on the evoked potential to perform hierarchical clustering on a set of word stimuli. The current study focuses on the semantic relationships that play a key role in the organization of our mental lexicon of words and concepts. The N400 component of the event-related potential is considered a reliable neurophysiological response that is indicative of whether accessing one concept facilitates subsequent access to another (i.e., one “primes” the other). To further our understanding of the organization of the human mental lexicon, we propose to utilize the N400 component to drive a clustering algorithm that can uncover, given a set of words, which particular subsets of words show mutual priming. Such a scheme requires a reliable measurement of the amplitude of the N400 component without averaging across many trials, which was here achieved using a recently developed multivariate analysis method based on beamforming. We validated our method by demonstrating that it can reliably detect, without any prior information about the nature of the stimuli, a well-known feature of the organization of our semantic memory: the distinction between animate and inanimate concepts. These results motivate further application of our method to data-driven exploration of disputed or unknown relationships between stimuli.

Amplitude of N400 component unaffected by lexical priming for moderately constraining sentences

The N400 is an event-related potential (ERP) that reflects the processing of semantics in the brain. When reading sentences, the N400 amplitude is modulated by both the cloze probability of the sentence and the association strength between individual words. When contradicted in strongly constraining sentences, that is, the beginning of the sentence builds a strong expectation of the final word; the cloze probability overrules the effect of association strength. We evidence that this is also the case for non-constraining sentences, such as the ones with low to moderate cloze probabilities. Our results give the evidences that if the sentence generates even weak to moderate expectations about the final word, word association plays almost no role in the processing of this word.