{'fraction_non_alphanumeric': 0.02710843373493976, 'fraction_numerical': 0.004518072289156626, 'mean_word_length': 5.10091743119266, '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}}
Evaluating the Mindwave Headset for Automatic Upper Body Motion Classification

This paper presents preliminary results on evaluating the NeuroSky Mindwave headset for upper body motion intention classification. An artificial neural network (ANN) is trained by using a data set built for two different feature extraction methods, one based on the wavelet transform (WT) and another based on the use of spectrograms. Since there are five different types of brain waves,(α, β, γ, Δ, θ) some data aggregation procedures are proposed to reduce the dimensionality of the data set. The classification results show that it is possible to attain a 73.1% of assertion rate.