Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG.

Investigation of Delayed Response during Real-Time Cursor Control Using Electroencephalography

Error-related brain activation has been investigated for advanced brain-machine interfaces (BMI). However, how a delayed response of cursor control in BMI systems should be handled is not clear. Therefore, the purpose of this study was to investigate how participants responded to delayed cursor control. Six subjects participated in the experiment and performed a wrist-bending task. For three distinct delay intervals (an interval where participants could not perceive the delay, an interval where participants could not be sure whether there was a delay or not, and an interval where participants could perceive the delay), we assessed two types of binary classifications (“Yes + No” vs. “I don't know” and “Yes” vs. “No”) based on participants' responses and applied delay times (thus, four types of classification, overall). For most participants, the “Yes vs. No” classification had higher accuracy than “Yes + No” vs. “I don't know” classification. For the “Yes + No” vs. “I don't know” classification, most participants displayed higher accuracy based on response classification than delay classification. Our results demonstrate that a class only for “I don't know” largely contributed to these differences. Many independent components (ICs) that exhibited high accuracy in “Yes + No” vs. “I don't know” response classification were associated with activation of areas from the frontal to parietal lobes, while many ICs that showed high accuracy in the “Yes vs. No” classification were associated with activation of an area ranging from the parietal to the occipital lobes and were more broadly localized in cortical regions than was seen for the “Yes + No” vs. “I don't know” classification. Our results suggest that small and large delays in real-time cursor control differ not only in the magnitude of the delay but should be handled as distinct information in different ways and might involve differential processing in the brain.