Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

Abstract

The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will. In a continuous stimulus set, 250 indoor/outdoor scenes were presented at random, between periods of fixation on a black background. The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image; both stimulus and response times were stored for analysis. The participants then performed a scene recognition test to confirm whether or not they remembered the images. The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene; p < 0.05 (two-tailed). This suggests that simultaneous EEG and fNIRS can be used for brain functional analysis, and MSE might be the potential indicator for this multimodality approach.

Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking

“…Thus, examining neural IIV in task-related research demonstrates both a clinical and methodological strength. Nonlinear measures of data complexity such as multi-scale entropy can provide additional information about the variability of data (Angsuwatanakul et al, 2020) and may be used in future studies. There is no current established entropy calculation method in fNIRS analysis, and many require large amounts of datapoints or other parameters not available in the current study.…”

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units

“…This is another perspective on the conventional view of entropy as being proportional to disorganisation, given that disorganised (changing) signals potentially contain more information than completely organised (unchanging) signals. Entropy measures have been used successfully to describe biological neural signals (Angsuwatanakul et al, 2020; Phukhachee et al, 2019); thus, this technique is appropriate for characterising the behaviour of artificial neural signals generated by the model.…”

Decoding violated sensory expectations from the auditory cortex of anaesthetized mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units

“…This is another perspective on the conventional view of entropy as being proportional to disorganisation, given that disorganised (changing) signals potentially contain more information than completely organised (unchanging) signals. Entropy measures have been used successfully to describe biological neural signals (Angsuwatanakul et al, 2020; Phukhachee et al, 2019), thus this technique is appropriate for characterising the behaviour of artificial neural signals generated by the model.…”