Latency analysis of single auditory evoked M100 responses by spatio-temporal filtering

Integrated trimodal SSEP experimental setup for visual, auditory and tactile stimulation

OBJECTIVE

Steady-state evoked potentials (SSEPs), the brain responses to repetitive stimulation, are commonly used in both clinical practice and scientific research. Particular brain mechanisms underlying SSEPs in different modalities (i.e. visual, auditory and tactile) are very complex and still not completely understood. Each response has distinct resonant frequencies and exhibits a particular brain topography. Moreover, the topography can be frequency-dependent, as in case of auditory potentials. However, to study each modality separately and also to investigate multisensory interactions through multimodal experiments, a proper experimental setup appears to be of critical importance. The aim of this study was to design and evaluate a novel SSEP experimental setup providing a repetitive stimulation in three different modalities (visual, tactile and auditory) with a precise control of stimuli parameters. Results from a pilot study with a stimulation in a particular modality and in two modalities simultaneously prove the feasibility of the device to study SSEP phenomenon.

APPROACH

We developed a setup of three separate stimulators that allows for a precise generation of repetitive stimuli. Besides sequential stimulation in a particular modality, parallel stimulation in up to three different modalities can be delivered. Stimulus in each modality is characterized by a stimulation frequency and a waveform (sine or square wave). We also present a novel methodology for the analysis of SSEPs.

MAIN RESULTS

Apart from constructing the experimental setup, we conducted a pilot study with both sequential and simultaneous stimulation paradigms. EEG signals recorded during this study were analyzed with advanced methodology based on spatial filtering and adaptive approximation, followed by statistical evaluation.

SIGNIFICANCE

We developed a novel experimental setup for performing SSEP experiments. In this sense our study continues the ongoing research in this field. On the other hand, the described setup along with the presented methodology is a considerable improvement and an extension of methods constituting the state-of-the-art in the related field. Device flexibility both with developed analysis methodology can lead to further development of diagnostic methods and provide deeper insight into information processing in the human brain.

Shifted factor analysis for the separation of evoked dependent MEG signals

Decomposition of evoked magnetoencephalography (MEG) data into their underlying neuronal signals is an important step in the interpretation of these measurements. Often, independent component analysis (ICA) is employed for this purpose. However, ICA can fail as for evoked MEG data the neuronal signals may not be statistically independent. We therefore consider an alternative approach based on the recently proposed shifted factor analysis model, which does not assume statistical independence of the neuronal signals. We suggest the application of this model in the time domain and present an estimation procedure based on a Taylor series expansion. We show in terms of synthetic evoked MEG data that the proposed procedure can successfully separate evoked dependent neuronal signals while standard ICA fails. Latency estimation of neuronal signals is an inherent part of the proposed procedure and we demonstrate that resulting latency estimates are superior to those obtained by a maximum likelihood method.