A Novel Equidistant-Scattering-Based Cluster Index

Flexa, Caio; Santos, Reginaldo; Gomes, Walisson; Sales, Claudomiro

doi:10.1109/bracis.2018.00099

Cited by 2 publications

(1 citation statement)

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“…The K-means algorithm was employed for this purpose. However, since the number of operators is limited, a non-parametric cluster validity index called Mutual Equidistant-scattering Criterion (MEC) is used to better determine the number of clusters [15].…”

Section: Operational Classes Definitionmentioning

confidence: 99%

Classification of productive profile using brain signals: Deep learning and neuroscience meets the mining industry

Siravenha¹,

Gomes²,

Tourinho³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Classification of electroencephalography (EEG) signals is a complex task. EEG is a non-stationary time process with low signal to noise ratio. Among many methods usedfor EEG classification, those based on Deep Learning (DL) have been relatively successful in providing high classification accuracies. In the present study we aimed at classify resting state EEGs measured from workers of a mining complex. Just after the EEG has been collected, the workers undergonetraining in a 4D virtual reality simulator that emulates the iron ore excavation from which parameters related to their performance were analyzed by the technical staff who classified the workers into four groups based on their productivity. Twoconvolutional neural networks (ConvNets) were then used to classify the workers EEG bases on the same productivity label provided by the technical staff. The neural data was used in three configurations in order to evaluate the amount of datarequired for a high accuracy classification. Isolated, the channel T5 achieved 83% of accuracy, the subtraction of channels P3 and Pz achieved 99% and using all channels simultaneously was 99.40% assertive. This study provides results that add to the recent literature showing that even simple DL architectures are able to handle complex time series such as the EEG. In addition, it pin points an application in industry with vast possibilities of expansion.

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Section: Operational Classes Definitionmentioning

confidence: 99%