Abstract
Analysis and collection of time-series data as a major role of machine learning has been emphasized with an important key in cognitive science. Because the cognitive mechanisms such as human sensation and perception from cognitive science are fast responses ranging from a few milliseconds to hundreds of milliseconds, the method of pattern recognition and analysis of these brain signals must be done and it is necessary to derive some information. In this paper, we investigated time-series data of cognitive function of the brain obtained using a non-invasive technique on multiple channels via signal classification and analysis, using a cognitive science approach and experiments. The test dataset was collected in 19 channels using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) techniques with multiple rests and working conditions on eight subjects. From this perspective, the main contributions of this paper are that it completes the collection and analysis of cognitive-scientific time-series data and has scientific implications that extend to other integrated domains, energy, manufacturing, bioinformatics, and finance area. The use of Shapelet and DTW (Dynamic Time Warping) classification techniques on brain signal time-series shows the potential to identify neuro-biological phenomena that can proactively signal a disease or disorder. EEG bandwidth and frequency-specific data have also been categorized as machine learning algorithms and have shown accurate patterns and trends in measuring cognitive functions of scientific, biological and academic importance.
Highlights
There are many ways to analyze data with a machine learning algorithm and reclassify it
To increase scientific value of this paper, this paper focused on time-series data analysis rather than recording human neurophysiological activity using two different modalities, EEG and functional near-infrared spectroscopy (fNIRS)
The fNIRS data time series was taken from another instrument and we can observe the tendency of the response slightly delayed overall than the EEG signal
Summary
There are many ways to analyze data with a machine learning algorithm and reclassify it. It is true that it is necessary to consider the most effective methodology to handle a large amount of data through bioinformatics. It is possible to produce scientific results if it is possible to measure and analyze changes in human cognition, such as perception and emotion, etc. Real-time analysis of brain functions medically, as well as in the domain of bioinformatics, has important implications. Epilepsy needs to be monitored on a regular basis to determine when and where seizures occur [1]. If the signal processes and machine learning methods discussed in this paper can be applied to predict the onset of epilepsy, preemptive treatment and countermeasures are possible
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