Assessing Feedback Response With a Wearable Electroencephalography System

Abstract

Background: Event related potential (ERP) components, such as P3, N2, and FRN, are potential metrics for assessing feedback response as a form of performance monitoring. Most research studies investigate these ERP components using clinical or research-grade electroencephalography (EEG) systems. Wearable EEGs, which are an affordable alternative, have the potential to assess feedback response using ERPs but have not been sufficiently evaluated. Feedback-related ERPs also have not been scientifically evaluated in interactive settings that are similar to daily computer use. In this study, a consumer-grade wearable EEG system was assessed for its feasibility to collect feedback-related ERPs through an interactive software module that provided an environment in which users were permitted to navigate freely within the program to make decisions. Methods: The recording hardware, which costs < $1,500 in total, incorporated the OpenBCI Cyton Board with Daisy chain, a consumer-grade EEG system that costs $949 USD. Seventeen participants interacted with an oddball paradigm and an interactive module designed to elicit feedback-related ERPs. The features of interests for the oddball paradigm were the P3 and N2 components. The features of interests for the interactive module were the P3, N2, and FRN components elicited in response to positive, neutral, and two types of negative feedback. The FRN was calculated by subtracting the positive feedback response from the negative feedback responses. Results: The P3 and N2 components of the oddball paradigm indicated statistically significant differences between infrequent targets and frequent targets which is in line with current literature. The P3 and N2 components elicited in the interactive module indicated statistically significant differences between positive, neutral, and negative feedback responses. There were no significant differences between the FRN types and significant interactions with channel group and FRN type. Conclusion: The OpenBCI Cyton, after some modifications, shows potential for eliciting and assessing P3, N2, and FRN components, which are important indicators for performance monitoring, in an interactive setting.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

“…Wearable EEG devices also have a higher level of integration compared to classical EEG devices because they are portable. Recent studies suggest that wearable EEG systems are capable of collecting neural signals, the quality of which is comparable with those collected by the traditional EEG systems in a controlled laboratory environment (Schiff et al, 2016 ; Kam et al, 2019 ; Qiu et al, 2019 ). However, studies show that wearable EEG systems are more prone to artifacts from muscle movements (Badcock et al, 2013 ; Ratti et al, 2017 ; Qiu et al, 2019 ).…”

“…Recent studies suggest that wearable EEG systems are capable of collecting neural signals, the quality of which is comparable with those collected by the traditional EEG systems in a controlled laboratory environment (Schiff et al, 2016;Kam et al, 2019;Qiu et al, 2019). However, studies show that wearable EEG systems are more prone to artifacts from muscle movements (Badcock et al, 2013;Ratti et al, 2017;Qiu et al, 2019). Some studies also suggest that the signal of wearable EEG might be delayed in comparison to traditional EEG systems (Qiu et al, 2019).…”

“…Compared to classical EEG systems, wearable EEG devices allow more liberty of movement for the subject. In addition, the average time for the preparation of the subject is less compared to traditional EEG (setup time of less than 5–6 min for Emotiv, Muse, and openBCI) (Qiu et al, 2019 ). Wearable EEG devices also have a higher level of integration compared to classical EEG devices because they are portable.…”

“…However, studies show that wearable EEG systems are more prone to artifacts from muscle movements (Badcock et al, 2013 ; Ratti et al, 2017 ; Qiu et al, 2019 ). Some studies also suggest that the signal of wearable EEG might be delayed in comparison to traditional EEG systems (Qiu et al, 2019 ). For academic purposes, the use of more complex (thus, more expensive) EEG systems, together with expert personnel, is highly recommended, since it provides a higher quality and a more reliable signal.…”

Wearable Multi-Biosignal Analysis Integrated Interface With Direct Sleep-Stage Classification

“…The comparison experiment was performed with the commercial product of the OpenBCI which provide highresolution EEG interface through the ADS1299 chip of the Texas Instrument. It is a kind of proven open-source device that has been utilized in various EEG researches [29]- [33]. As shown in Fig.…”