Real-Time Decoding of Auditory Attention from EEG via Bayesian Filtering

A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification

Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain-computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.

Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task

In the last few years, a large number of experiments have been focused on exploring the possibility of using non-invasive techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to identify auditory-related neuromarkers which are modulated by attention. Results from several studies where participants listen to a story narrated by one speaker, while trying to ignore a different story narrated by a competing speaker, suggest the feasibility of extracting neuromarkers that demonstrate enhanced phase locking to the attended speech stream. These promising findings have the potential to be used in clinical applications, such as EEG-driven hearing aids. One major challenge in achieving this goal is the need to devise an algorithm capable of tracking these neuromarkers in real-time when individuals are given the freedom to repeatedly switch attention among speakers at will. Here we present an algorithm pipeline that is designed to efficiently recognize changes of neural speech tracking during a dynamic-attention switching task and to use them as an input for a near real-time state-space model that translates these neuromarkers into attentional state estimates with a minimal delay. This algorithm pipeline was tested with MEG data collected from participants who had the freedom to change the focus of their attention between two speakers at will. Results suggest the feasibility of using our algorithm pipeline to track changes of attention in near-real time in a dynamic auditory scene.