{'fraction_non_alphanumeric': 0.023255813953488372, 'fraction_numerical': 0.0018604651162790699, 'mean_word_length': 5.601226993865031, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detecting Neural Decision Patterns Using SVM-Based EEG Classification

Brain dynamics were analyzed during decision making using human electroencephalographic signals. We sought to identify the pattern of brain activity for actions with and without decision-making, while subjects engaged in an instrumental reward based learning task. Event related potentials (ERPs) were analyzed for reference trials (no choice required) and decision trials. To detect brain activity during decision making, classification was applied to classify reference and decision trials. Support vector machine (SVM) with a nonlinear kernel function was used as a classifier. Classification performance was analyzed across subjects and channels to identify brain regions underlying decision-making. For most subjects, we found that reference and decision trials could be classified with greater than 85% accuracy. ERPs from frontocentral areas of the scalp provided, in general, best classification rates. Thus ERPs and SVM classifiers can be used to non-invasively detect decision making in humans.