100 results for "signal classification" · page 7 of 10 · ranked by relevance
…target and non-target event classifications. ## Dataset Summary | Property | Value | |---|---| | Subjects | 19…
…computer interface applications, achieving mean classification accuracy of 66.2±4.8…
…computer interface applications, achieving mean classification accuracy of 73.3±8.9…
…categories through single-trial EEG classification and representational similarity analysis. ## Dataset Summary…
…raw Signal Processing ----------------- Classifiers: Convolutional Neural Network (CNN) Feature extraction: Sliding windows…
…p300 ## Signal Processing - **Feature extraction**: P300_ERP_detection ## Cross-Validation - **Method**: calibration…
…p300 Signal Processing ----------------- Feature extraction: P300_ERP_detection Cross-Validation ---------------- Method: calibration…
MorseEEG-ATP is a multitask electroencephalography dataset comprising synchronously recorded EEG and behavioral data from 32 healthy adult participants designed to investigate auditory temporal processing and temporal pattern discrimination using Morse code stimuli. The dataset includes raw 62-channel EEG recordings at 600 Hz, minimally preprocessed derivatives, trial-aligned event annotations, and behavioral measures across two discrimination tasks (dot-count and first-last match) presented at three transmission speeds (15, 30, and 40 characters per minute). This resource bridges the gap between low-level acoustic encoding and higher-level cognitive processing by providing a controlled, parameterizable paradigm for studying the extraction and discrimination of temporal patterns governed by explicit temporal rules.