The N400 for Brain Computer Interfacing: complexities and opportunities

The N400 is an Event Related Potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. Given this potential it is notable that the BCI literature exploiting the N400 is limited. We identify three existing application areas: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user's mind. Drawing on studies from these application areas, we illustrate that the N400 can successfully be exploited for BCI purposes, but that the signal-to-noise ratio is a limiting factor, with signal strength also varying strongly across subjects. Furthermore, we put findings in context of the general N400 literature, noting open questions and identifying opportunities for further research.Comment: 28 pages, 2 figures, 2 table

Electrophysiological model of human temporal contrast sensitivity based on SSVEP

The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1–50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40–60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions

Robust brain-computer interfaces

A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Current BCIs aimed at patients require that the user invests weeks, or even months, to learn the skill to intentionally modify their brain signals. This can be reduced to a calibration session of about half an hour per session if machine learning (ML) methods are used. The laborious recalibration is still needed due to inter-session differences in the statistical properties of the electroencephalography (EEG) signal. Further, the natural variability in spontaneous EEG violates basic assumptions made by the ML methods used to train the BCI classifier, and causes the classification accuracy to fluctuate unpredictably. These fluctuations make the current generation of BCIs unreliable. In this dissertation,we will investigate the nature of these variations in the EEG distributions, and introduce two new, complementary methods to overcome these two key issues. To confirm the problem of non-stationary brain signals, we first show that BCIs based on commonly used signal features are sensitive to changes in the mental state of the user. We proceed by describing a method aimed at removing these changes in signal feature distributions. We have devised a method that uses a second-order baseline (SOB) to specifically isolate these relative changes in neuronal firing synchrony. To the best of our knowledge this is the first BCI classifier that works on out-of-sample subjects without any loss of performance. Still, the assumption made by ML methods that the training data consists of samples that are independent and identically distributed (iid) is violated, because EEG samples nearby in time are highly correlated. Therefore we derived a generalization of the well-known support vector machine (SVM) classifier, that takes the resulting chronological structure of classification errors into account. Both on artificial data and real BCI data, overfitting is reduced with this dependent samples support vector machine (dSVM), leading to BCIs with an increased information throughput

A connectionist and a traditional AI quantizer, symbolic versus sub-symbolic models of rhythm perception

Contains fulltext : 74722.pdf (publisher's version ) (Open Access)The Symbolic AI paradigm and the Connectionist paradigm have produced some incompatible models of the same domain of cognition. Two such models in the field of rhythm perception, namely the Longuet-Higgins Musical Parser and the Desain & Honing connectionist quantizer, were studied in order to find ways to compare and evaluate them. Different perspectives from which to describe their behavior were developed, providing a conceptual as well as a visual representation of the operation of the models. With these tools it proved possible to discuss their similarities and differences and to narrow the gap between sub-symbolic and symbolic models.Themanr. o.d.t. Music and the Cognitive Science