Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.

Ear-EEG-based binaural speech enhancement (ee-BSE) using auditory attention detection and audiometric characteristics of hearing-impaired subjects

Objective. Speech perception in cocktail party scenarios has been the concern of a group of researchers who are involved with the design of hearing-aid devices.Approach. In this paper, a new unified ear-EEG-based binaural speech enhancement system is introduced for hearing-impaired (HI) listeners. The proposed model, which is based on auditory attention detection (AAD) and individual hearing threshold (HT) characteristics, has four main processing stages. In the binaural processing stage, a system based on the deep neural network is trained to estimate auditory ratio masks for each of the speakers in the mixture signal. In the EEG processing stage, AAD is employed to select one ratio mask corresponding to the attended speech. Here, the same EEG data is also used to predict the HTs of listeners who participated in the EEG recordings. The third stage, called insertion gain computation, concerns the calculation of a special amplification gain based on individual HTs. Finally, in the selection-resynthesis-amplification stage, the attended speech signals of the target are resynthesized based on the selected auditory mask and then are amplified using the computed insertion gain.Main results. The detection of the attended speech and the HTs are achieved by classifiers that are trained with features extracted from the scalp EEG or the ear EEG signals. The results of evaluating AAD and HT detection show high detection accuracies. The systematic evaluations of the proposed system yield substantial intelligibility and quality improvements for the HI and normal-hearingaudiograms.Significance. The AAD method determines the direction of attention from single-trial EEG signals without access to audio signals of the speakers. The amplification procedure could be adjusted for each subject based on the individual HTs. The present model has the potential to be considered as an important processing tool to personalize the neuro-steered hearing aids.

Feature-Selection-Based Transfer Learning for Intracortical Brain-Machine Interface Decoding

The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). Decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work; thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.

Tensor Based Blind Source Separation in Longitudinal Magnetic Resonance Imaging Analysis

Study of white matter (WM) fiber-bundles is a crucial challenge in the investigation of neurological diseases like multiple sclerosis (MS). In this activity, the amount of data to process is huge, and an automated approach to carry out this task is in order.In this paper we show how tensor-based blind source separation (BSS) techniques can be successfully applied to model complex anatomical brain structures. More in detail, we show how through vector hankelization it is possible to formalize data extracted from WM fiber-bundles using a tensor model. Two main tensor factorization techniques, namely (L_r, L_r, 1) block term decomposition (BTD) and canonical polyadic decomposition (CPD), were applied to the generated tensor. The information extracted from the factorization was then used to differentiate between sets of fibers, within the bundle, affected by the pathology and normal appearing fibers.Performances of the proposed tensor-based model was evaluated on simulated data representing pathological effects of MS. Results show the capability of our tensor-based model to detect small pathological phenomena appearing along WM fibers.

How Does the Degree of Valence Influence Affective Auditory P300-Based BCIs?

A brain-computer interface (BCI) translates brain signals into commands for the control of devices and for communication. BCIs enable persons with disabilities to communicate externally. Positive and negative affective sounds have been introduced to P300-based BCIs; however, how the degree of valence (e.g., very positive or positive) influences the BCI has not been investigated. To further examine the influence of affective sounds in P300-based BCIs, we applied sounds with five degrees of valence to the P300-based BCI. The sound valence ranged from very negative to very positive, as determined by Scheffe's method. The effect of sound valence on the BCI was evaluated by waveform analyses, followed by the evaluation of offline stimulus-wise classification accuracy. As a result, the late component of P300 showed significantly higher point-biserial correlation coefficients in response to very positive and very negative sounds than in response to the other sounds. The offline stimulus-wise classification accuracy was estimated from a region-of-interest. The analysis showed that the very negative sound achieved the highest accuracy and the very positive sound achieved the second highest accuracy, suggesting that the very positive sound and the very negative sound may be required to improve the accuracy.