Motor restrictions impair divergent thinking during walking and during sitting

Creativity, specifically divergent thinking, has been shown to benefit from unrestrained walking. Despite these findings, it is not clear if it is the lack of restriction that leads to the improvement. Our goal was to explore the effects of motor restrictions on divergent thinking for different movement states. In addition, we assessed whether spontaneous eye blinks, which are linked to motor execution, also predict performance. In experiment 1, we compared the performance in Guilford’s alternate uses task (AUT) during walking vs. sitting, and analysed eye blink rates during both conditions. We found that AUT scores were higher during walking than sitting. Albeit eye blinks differed significantly between movement conditions (walking vs. sitting) and task phase (baseline vs. thinking vs. responding), they did not correlate with task performance. In experiment 2 and 3, participants either walked freely or in a restricted path, or sat freely or fixated on a screen. When the factor restriction was explicitly modulated, the effect of walking was reduced, while restriction showed a significant influence on the fluency scores. Importantly, we found a significant correlation between the rate of eye blinks and creativity scores between subjects, depending on the restriction condition. Our study shows a movement state-independent effect of restriction on divergent thinking. In other words, similar to unrestrained walking, unrestrained sitting also improves divergent thinking. Importantly, we discuss a mechanistic explanation of the effect of restriction on divergent thinking based on the increased size of the focus of attention and the consequent bias towards flexibility.

Hippocampal Volume, Neurite Density and Trace Eye-Blink Conditioning in Young Children

The current study examined the relations between hippocampal structure (e.g., volume and neurite density) and performance on a trace eye blink conditioning (EBC) task in young children. Our first aim assessed whether individual differences in hippocampal volume were associated with trace EBC performance, using both percent Conditioned Responses (% CR) and CR onset latency or the average latency (ms) at which the child started their blink, as measures of hippocampal-dependent associative learning. Our second aim evaluated whether individual differences in hippocampal neurite density were associated with EBC performance using the same outcome measures. Typically developing 4- to 6-year-olds (N = 31; 14 girls; Mage = 5.67; SDage = 0.89) completed T1 and diffusion-weighted MRI scans and a 15-minute trace eyeblink conditioning task outside of the scanner. % CR and CR onset latency were computed across all tone-puff and tone-alone trials. While hippocampal volume was not associated with any of our EBC measures, greater hippocampal neurite density bilaterally, was associated with later CR onset. In other words, children with greater left and right hippocampal neurite density blinked closer to the US (i.e., air puff) than children with less hippocampal neurite density, indicating that structural changes in the hippocampus assisted in the accurate timing of conditioned responses.

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

Eye Blink Artefact Removal of Single Frontal EEG Channel Algorithm using Ensemble Empirical Mode Decomposition and Outlier Detection

Recently, the emergence of various applications to use EEG has evolved the EEG device to become wearable with fewer electrodes. Unfortunately, the process of removing artefact becomes challenging since the conventional method requires an additional artefact reference channel or multichannel recording to be working. By focusing on frontal EEG channel recording, this paper proposed an alternative single-channel eye blink artefact removal method based on the ensemble empirical mode decomposition and outlier detection technique. The method removes the segment of the potential eyeblinks artefact on the residual of a pre-determined level of decomposition. An outlier detection technique is introduced to identify the peak of the eyeblink based on the extreme value of the residual signal. The results showed that the corrected EEG signal achieved high correlation, low RMSE and have small differences in PSD when compared to the reference clean EEG. Comparing with an adaptive Wiener filter technique, the corrected EEG signal by the proposed method had better signal-to-artefact ratio.

Wheelchair controlled by human brainwave using brain-computer interface system for paralyzed patient

Integrated wheelchair controlled by human brainwave using a brain-computer interface (BCI) system was designed to help disabled people. The invention aims to improve the development of integrated wheelchair using a BCI system, depending on the ability individual brain attention level. An electroencephalography (EEG) device called mindwave mobile plus (MW+) has been employed to obtain the attention value for wheelchair movement, eye blink to change the mode of the wheelchair to move forward (F), to the right (R), backward (B) and to the left (L). Stop mode (S) is selected when doing eyebrow movement as the signal quality value of 26 or 51 is produced. The development of the wheelchair controlled by human brainwave using a BCI system for helping a paralyzed patient shows the efficiency of the brainwave integrated wheelchair and improved using human attention value, eye blink detection and eyebrow movement. Also, analysis of the human attention value in different gender and age category also have been done to improve the accuracy of the brainwave integrated wheelchair. The threshold value for male children is 60, male teenager (70), male adult (40) while for female children is 50, female teenager (50) and female adult (30).

EEG Multipurpose Eye Blink Detector using convolutional neural network

The electrical signal emitted by the eyes movement produces a very strong artifact on EEG signal due to its close proximity to the sensors and abundance of occurrence. In the context of detecting eye blink artifacts in EEG waveforms for further removal and signal purification, multiple strategies where proposed in the literature. Most commonly applied methods require the use of a large number of electrodes, complex equipment for sampling and processing data. The goal of this work is to create a reliable and user independent algorithm for detecting and removing eye blink in EEG signals using CNN (convolutional neural network). For training and validation, three sets of public EEG data were used. All three sets contain samples obtained while the recruited subjects performed assigned tasks that included blink voluntarily in specific moments, watch a video and read an article. The model used in this study was able to have an embracing understanding of all the features that distinguish a trivial EEG signal from a signal contaminated with eye blink artifacts without being overfitted by specific features that only occurred in the situations when the signals were registered.