A validation of Emotiv EPOC Flex saline for EEG and ERP research

The effect of a spinal thrust manipulation's audible pop on brain wave activity: a quasi-experimental repeated measure design

Introduction

High velocity thrust manipulation is commonly used when managing joint dysfunctions. Often, these thrust maneuvers will elicit an audible pop. It has been unclear what conclusively causes this audible sound and its clinical meaningfulness. This study sought to identify the effect of the audible pop on brainwave activity directly following a prone T7 thrust manipulation in asymptomatic/healthy subjects.

Methods

This was a quasi-experimental repeated measure study design in which 57 subjects completed the study protocol. Brain wave activity was measured with the Emotiv EPOC+, which collects data with a frequency of 128 HZ and has 14 electrodes. Testing was performed in a controlled environment with minimal electrical interference (as measured with a Gauss meter), temperature variance, lighting variance, sound pollution, and other variable changes that could have influenced or interfered with pure EEG data acquisition. After accommodation each subject underwent a prone T7 posterior-anterior thrust manipulation. Immediately after the thrust manipulation the brainwave activity was measured for 10 seconds.

Results

The non-audible group (N = 20) consisted of 55% males, and the audible group (N = 37) consisted of 43% males. The non-audible group EEG data revealed a significant change in brain wave activity under some of the electrodes in the frontal, parietal, and the occipital lobes. In the audible group, there was a significant change in brain wave activity under all electrodes in the frontal lobes, the parietal lobe, and the occipital lobes but not the temporal lobes.

Conclusion

The audible sounds caused by a thoracic high velocity thrust manipulation did not affect the activity in the audible centers in the temporal brain region. The results support the hypothesis that thrust manipulation with or without audible sound results in a generalized relaxation immediately following the manipulation. The absence of a significant difference in brainwave activity in the frontal lobe in this study might indicate that the audible pop does not produce a "placebo" mechanism.

Enhancing Working Memory and Reducing Anxiety in University Students: A Neurofeedback Approach

(1) Background: Neurofeedback training (NFT) has emerged as a promising approach for enhancing cognitive functions and reducing anxiety, yet its specific impact on university student populations requires further investigation. This study aims to examine the effects of NFT on working memory improvement and anxiety reduction within this demographic. (2) Methods: A total of forty healthy university student volunteers were randomized into two groups: an experimental group that received NFT and a control group. The NFT protocol was administered using a 14-channel Emotiv Epoc X headset (EMOTIV, Inc., San Francisco, CA 94102, USA) and BrainViz software version Brain Visualizer 1.1 (EMOTIV, Inc., San Francisco, CA 94102, USA), focusing on the alpha frequency band to target improvements in working memory and reductions in anxiety. Assessment tools, including the Corsi Block and Memory Span tests for working memory and the State-Trait Anxiety Inventory-2 (STAI-2) for anxiety, were applied pre- and post-intervention. (3) Results: The findings indicated an increase in alpha wave amplitude in the experimental group from the second day of NFT, with statistically significant differences observed on days 2 (p < 0.05) and 8 (p < 0.01). Contrary to expectations based on the previous literature, the study did not observe a concurrent positive impact on working memory. Nonetheless, a significant reduction in state anxiety levels was recorded in the experimental group (p < 0.001), corroborating NFT's potential for anxiety management. (4) Conclusions: While these results suggest some potential of the technique in enhancing neural efficiency, the variability across different days highlights the need for further investigation to fully ascertain its effectiveness. The study confirms the beneficial impact of NFT on reducing state anxiety among university students, underscoring its value in psychological and cognitive performance enhancement. Despite the lack of observed improvements in working memory, these results highlight the need for continued exploration of NFT applications across different populations and settings, emphasizing its potential utility in educational and therapeutic contexts.

Methodology and Experimental Protocol for Studying Learning and Motor Control in Neuromuscular Structures in Pilates

The benefits of Pilates have been extensively researched for their impact on muscular, psychological, and cardiac health, as well as body composition, among other aspects. This study aims to investigate the influence of the Pilates method on the learning process, motor control, and neuromuscular trunk stabilization, specifically in both experienced and inexperienced practitioners. This semi-randomized controlled trial compares the level of experience among 36 Pilates practitioners in terms of motor control and learning of two Pilates-based skills: standing plank and side crisscross. Data will be collected using various assessment methods, including abdominal wall muscle ultrasound (AWMUS), shear wave elastography (SWE), gaze behavior (GA) assessment, electroencephalography (EEG), and video motion. Significant intra- and inter-individual variations are expected, due to the diverse morphological and psychomotor profiles in the sample. The adoption of both linear and non-linear analyses will provide a comprehensive evaluation of how neuromuscular structures evolve over time and space, offering both quantitative and qualitative insights. Non-linear analysis is expected to reveal higher entropy in the expert group compared to non-experts, signifying greater complexity in their motor control. In terms of stability, experts are likely to exhibit higher Lyapunov exponent values, indicating enhanced stability and coordination, along with lower Hurst exponent values. In elastography, experienced practitioners are expected to display higher transversus abdominis (TrA) muscle elasticity, due to their proficiency. Concerning GA, non-experts are expected to demonstrate more saccades, focus on more Areas of Interest (AOIs), and shorter fixation times, as experts are presumed to have more efficient gaze control. In EEG, we anticipate higher theta wave values in the non-expert group compared to the expert group. These expectations draw from similar studies in elastography and correlated research in eye tracking and EEG. They are consistent with the principles of the Pilates Method and other scientific knowledge in related techniques.

A novel wearable ERP-based BCI approach to explicate hunger necessity

This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms. We compared the classification performances and reached the best-performing algorithm. The highest success score of 97.62% was achieved using the Multilayer Perceptron Neural Network algorithm in Event-Related Potential analysis.

Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions

The constantly evolving technological landscape of the Metaverse has introduced a significant concern: cybersickness (CS). There is growing academic interest in detecting and mitigating these adverse effects within virtual environments (VEs). However, the development of effective methodologies in this field has been hindered by the lack of sufficient benchmark datasets. In pursuit of this objective, we meticulously compiled a comprehensive dataset by analyzing the impact of virtual reality (VR) environments on CS, immersion levels, and EEG-based emotion estimation. Our dataset encompasses both implicit and explicit measurements. Implicit measurements focus on brain signals, while explicit measurements are based on participant questionnaires. These measurements were used to collect data on the extent of cybersickness experienced by participants in VEs. Using statistical methods, we conducted a comparative analysis of CS levels in VEs tailored for specific tasks and their immersion factors. Our findings revealed statistically significant differences between VEs, highlighting crucial factors influencing participant engagement, engrossment, and immersion. Additionally, our study achieved a remarkable classification performance of 96.25% in distinguishing brain oscillations associated with VR scenes using the multi-instance learning method and 95.63% in predicting emotions within the valence-arousal space with four labels. The dataset presented in this study holds great promise for objectively evaluating CS in VR contexts, differentiating between VEs, and providing valuable insights for future research endeavors.

Opposite effects of a time-trial and endurance cycling exercise on the neural efficiency of competitive cyclists

PURPOSE

Time-trial require cyclists to have an acute control on their sensory cues to regulate their pacing strategies. Pacing an effort accurately requires an individual to process sensory signals with efficacy, a factor that can be characterized by a high neural efficiency. This study aimed to investigate the effect of a cycling time-trial on neural efficiency in comparison to a low intensity endurance exercise, the latter supposedly not requiring high sensory control.

METHODS

On two separate days, 13 competitive cyclists performed a session comprising of two 10 min treadmill tests, performed at different intensity zones from 1 to 5 on the rating subjective exercise intensity scale. The tests were performed before and after both a time-trial and endurance cycling exercise. Electroencephalography activity was measured during each intensity zones of the treadmill exercises. Neural efficiency was then calculated for each intensity block using the α/β electroencephalography activity ratio.

RESULTS

The neural efficiency averaged on the 5 IZ decreased following the time-trial in the motor cortex (- 13 ± 8%) and prefrontal cortex (- 10 ± 12%), but not after the endurance exercise.

CONCLUSION

To conclude, the time-trial impaired the neural efficiency and increasing the RPE of the cyclists in the severe intensity zone.

Crowdsourced EEG experiments: A proof of concept for remote EEG acquisition using EmotivPRO Builder and EmotivLABS

The development of online research platforms has made data collection more efficient and representative of populations. However, these benefits have not been available for use with cognitive neuroscience tools such as electroencephalography (EEG). In this study, we introduce an approach for remote EEG data collection. We demonstrate how an experiment can be built via the EmotivPRO Builder and deployed to the EmotivLABS website where it can be completed by participants who own EMOTIV EEG headsets. To demonstrate the data collection technique, we collected EEG while participants engaged in a resting state task where participants sat with their eyes open and then eyes closed for 2 min each. We observed a significant difference in alpha power between the two conditions thereby demonstrating the well-known alpha suppression effect. Thus, we demonstrate that EEG data collection, particularly for frequency domain analysis, can be successfully conducted online.

Remote collection of electrophysiological data with brain wearables: opportunities and challenges

Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.

A resting state EEG study on depressed persons with suicidal ideation

Background

Major Depressive Disorder (MDD) is a psychiatric illness that is often associated with potentially life-threatening physiological changes and increased risk for suicidal behavior. Electroencephalography (EEG) research suggests an association between depression and specific frequency imbalances in the frontal brain region. Further, while recently developed technology has been proposed to simplify EEG data acquisition, more research is still needed to support its use in patients with MDD.

Methods

Using the 14-channel EMOTIV EPOC cap, we recorded resting state EEG from 15 MDD patients with and MDD persons with suicidal ideation (SI) vs. 12 healthy controls (HC) to investigate putative power spectral density (PSD) between-group differences at the F3 and F4 electrode sites. Specifically, we explored 1) between-group alpha power asymmetries (AA), 2) between-group differences in delta, theta, alpha and beta power, 3) between PSD data and the scores in the Beck's Depression Inventory-II (BDI-II), Beck's Anxiety Inventory (BAI), Reasons for Living Inventory (RFL), and Self-Disgust Questionnaire (SDS).

Results

When compared to HC, patients had higher scores on the BAI (p = 0.0018), BDI-II (p = 0.0001) or SDS (p = 0.0142) scale and lower scores in the RFL (p = 0.0006) scale. The PSD analysis revealed no between-group difference or correlation with questionnaire scores for any of the measures considered.

Conclusions

The present study could not confirm previous research suggesting frequency-specific anomalies in depressed persons with SI but might suggest that frontal EEG imbalances reflect greater anxiety and negative self-referencing. Future studies should confirm these findings in a larger population sample.

How low can you go? Measuring human event-related brain potentials from a two-channel EEG system

Over the past ten years, there has been a rapid increase in the availability and use of mobile electroencephalography (mEEG) in research. Indeed, researchers using mEEG have recorded EEG and event-related brain potentials in a wide range of environments - for example, while walking (Debener et al., 2012), riding a bike (Scanlon et al., 2020), or even in a shopping mall (Krigolson et al., 2021). However, given that low-cost, ease-of-use, and setup speed provide the primary advantages of an mEEG system over large array traditional EEG systems, an important and unresolved question is just how many electrodes does an mEEG system need to collect research-quality EEG data? Here, we tested whether or not a two-channel forehead-mounted mEEG system - the "Patch" - could measure event-related brain potentials within their established amplitude and latency characteristics (Luck, 2014). In the present study, participants performed a visual oddball task while we recorded EEG data from the Patch. Our results demonstrated that we could capture and quantify the N200 and P300 event-related brain potential components using a minimal electrode array forehead-mounted EEG system. Our data further support the idea that mEEG can be used for quick and rapid EEG-based assessments, such as measuring the impact of concussions on the sports field (Fickling et al., 2021) or assessing the impact of stroke severity in a hospital (Wilkinson et al., 2020).

Repeatability of Brain Activity as Measured by a 32-Channel EEG System during Resistance Exercise in Healthy Young Adults

Electroencephalography (EEG) is attracting increasing attention in the sports and exercise fields, as it provides insights into brain behavior during specific tasks. However, it remains unclear if the promising wireless EEG caps provide reliable results despite the artifacts associated with head movement. The present study aims to evaluate the repeatability of brain activity as measured by a wireless 32-channel EEG system (EMOTIV flex cap) during resistance exercises in 18 apparently healthy but physically inactive young adults (10 men and 8 women). Moderate-intensity leg press exercises are performed with two evaluations with 48 h. between. This intensity allows enough time for data analysis while reducing unnecessary but involuntary head movements. Repeated measurements of EEG during the resistance exercise show high repeatability in all frequency bands, with excellent ICCs (>0.90) and bias close to zero, regardless of sex. These results suggest that a 32-channel wireless EEG system can be used to collect data on controlled resistance exercise tasks performed at moderate intensities. Future studies should replicate these results with a bigger sample size and different resistance exercises and intensities.

ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods

The incidence of maritime accidents can be significantly reduced by identifying the deck officer's fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver's fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers' eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers' fatigue level, which combines a video-based approach to record the officer's eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of "fatigue" at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers' fatigue levels finally reaches 95.74 percent.

Black hole algorithm with convolutional neural networks for the creation of brain-computer interface based in visual perception and visual imagery

Non-invasive brain-computer interfaces can be implemented through different paradigms, the most used one being motor imagery and evoked potentials, although recently there has been an interest in paradigms based on perception and visual imagery. Following this approach, this work demonstrates the classification of visual imagery, visual perception and also the possibility of knowledge transfer between these two domains from EEG signals using convolutional neural networks. Also, we propose an adequate framework for such classification, which uses convolutional neural networks and the black hole heuristic algorithm for the search for optimal neural network structures.

CNN-Based Personal Identification System Using Resting State Electroencephalography

As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.

It's all about time: precision and accuracy of Emotiv event-marking for ERP research

Background

The use of consumer-grade electroencephalography (EEG) systems for research purposes has become more prevalent. In event-related potential (ERP) research, it is critical that these systems have precise and accurate timing. The aim of the current study was to investigate the timing reliability of event-marking solutions used with Emotiv commercial EEG systems.

Method

We conducted three experiments. In Experiment 1 we established a jitter threshold (i.e. the point at which jitter made an event-marking method unreliable). To do this, we introduced statistical noise to the temporal position of event-marks of a pre-existing ERP dataset (recorded with a research-grade system, Neuroscan SynAmps² at 1,000 Hz using parallel-port event-marking) and calculated the level at which the waveform peaks differed statistically from the original waveform. In Experiment 2 we established a method to identify ‘true’ events (i.e. when an event should appear in the EEG data). We did this by inserting 1,000 events into Neuroscan data using a custom-built event-marking system, the ‘Airmarker’, which marks events by triggering voltage spikes in two EEG channels. We used the lag between Airmarker events and events generated by Neuroscan as a reference for comparisons in Experiment 3. In Experiment 3 we measured the precision and accuracy of three types of Emotiv event-marking by generating 1,000 events, 1 s apart. We measured precision as the variability (standard deviation in ms) of Emotiv events and accuracy as the mean difference between Emotiv events and true events. The three triggering methods we tested were: (1) Parallel-port-generated TTL triggers; (2) Arduino-generated TTL triggers; and (3) Serial-port triggers. In Methods 1 and 2 we used an auxiliary device, Emotiv Extender, to incorporate triggers into the EEG data. We tested these event-marking methods across three configurations of Emotiv EEG systems: (1) Emotiv EPOC+ sampling at 128 Hz; (2) Emotiv EPOC+ sampling at 256 Hz; and (3) Emotiv EPOC Flex sampling at 128 Hz.

Results

In Experiment 1 we found that the smaller P1 and N1 peaks were attenuated at lower levels of jitter relative to the larger P2 peak (21 ms, 16 ms, and 45 ms for P1, N1, and P2, respectively). In Experiment 2, we found an average lag of 30.96 ms for Airmarker events relative to Neuroscan events. In Experiment 3, we found some lag in all configurations. However, all configurations exhibited precision of less than a single sample, with serial-port-marking the most precise when paired with EPOC+ sampling at 256 Hz.

Conclusion

All Emotiv event-marking methods and configurations that we tested were precise enough for ERP research as the precision of each method would provide ERP waveforms statistically equivalent to a research-standard system. Though all systems exhibited some level of inaccuracy, researchers could easily account for these during data processing.

Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation

Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.