Alpha Oscillations During Effortful Continuous Speech: From Scalp EEG to Ear-EEG.

OBJECTIVE: The purpose of this study was to investigate alpha power as an objective measure of effortful listening in continuous speech with scalp and ear-EEG. METHODS: Scalp and ear-EEG were recorded simultaneously during presentation of a 33-s news clip in the presence of 16-talker babble noise. Four different signal-to-noise ratios (SNRs) were used to manipulate task demand. The effects of changes in SNR were investigated on alpha event-related synchronization (ERS) and desynchronization (ERD). Alpha activity was extracted from scalp EEG using different referencing methods (common average and symmetrical bi-polar) in different regions of the brain (parietal and temporal) and ear-EEG. RESULTS: Alpha ERS decreased with decreasing SNR (i.e., increasing task demand) in both scalp and ear-EEG. Alpha ERS was also positively correlated to behavioural performance which was based on the questions regarding the contents of the speech. CONCLUSION: Alpha ERS/ERD is better suited to track performance of a continuous speech than listening effort. SIGNIFICANCE: EEG alpha power in continuous speech may indicate of how well the speech was perceived and it can be measured with both scalp and Ear-EEG.

Single-trial classification of auditory event-related potentials elicited by stimuli from different spatial directions.

This study is focused on the single-trial classification of auditory event-related potentials elicited by sound stimuli from different spatial directions. Five naϊve subjects were asked to localize a sound stimulus reproduced over one of 8 loudspeakers placed in a circular array, equally spaced by 45°. The subject was seating in the center of the circular array. Due to the complexity of an eight classes classification, our approach consisted on feeding our classifier with two classes, or spatial directions, at the time. The seven chosen pairs were 0°, which was the loudspeaker directly in front of the subject, with all the other seven directions. The discrete wavelet transform was used to extract features in the time-frequency domain and a support vector machine performed the classification procedure. The average accuracy over all subjects and all pair of spatial directions was 76.5%, σ = 3.6. The results of this study provide evidence that the direction of a sound is encoded in single-trial auditory event-related potentials.

Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery.

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r (2)). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r(2) for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery.

Auditory and spatial navigation imagery in Brain-Computer Interface using optimized wavelets.

Features extracted with optimized wavelets were compared with standard methods for a Brain-Computer Interface driven by non-motor imagery tasks. Two non-motor imagery tasks were used, Auditory Imagery of a familiar tune and Spatial Navigation Imagery through a familiar environment. The aims of this study were to evaluate which method extracts features that could be best differentiated and determine which channels are best suited for classification. EEG activity from 18 electrodes over the temporal and parietal lobes of nineteen healthy subjects was recorded. The features used were autoregressive and reflection coefficients extracted using autoregressive modeling with several model orders and marginals of the wavelet spaces generated by the Discrete Wavelet Transform (DWT). An optimization algorithm with 4 and 6 taps filters and mother wavelets from the Daubechies family were used. The classification was performed for each single channel and for all possible combination of two channels using a Bayesian Classifier. The best classification results were found using the marginals of the Optimized DWT spaces for filters with 6 taps in a 2 channels classification basis. Classification using 2 channels was found to be significantly better than using 1 channel (p<<0.01). The marginals of the optimized DWT using 6 taps filters showed to be significantly better than the marginals of the Daubechies family and autoregressive coefficients. The influence of the combination of number of channels and feature extraction method over the classification results was not significant (p=0.97).

EEG based BCI-towards a better control. Brain-computer interface research at Aalborg University.

This paper summarizes the brain-computer interface (BCI)-related research being conducted at Aalborg University. Namely, an online synchronized BCI system using steady-state visual evoked potentials, and investigations on cortical modulation of movement-related parameters are presented.