Identifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experiments

Recent developments in EEG hardware and analyses approaches allow for recordings in both stationary and mobile settings. Irrespective of the experimental setting, EEG recordings are contaminated with noise that has to be removed before the data can be functionally interpreted. Independent component analysis (ICA) is a commonly used tool to remove artifacts such as eye movement, muscle activity, and external noise from the data and to analyze activity on the level of EEG effective brain sources. The effectiveness of filtering the data is one key preprocessing step to improve the decomposition that has been investigated previously. However, no study thus far compared the different requirements of mobile and stationary experiments regarding the preprocessing for ICA decomposition. We thus evaluated how movement in EEG experiments, the number of channels, and the high‐pass filter cutoff during preprocessing influence the ICA decomposition. We found that for commonly used settings (stationary experiment, 64 channels, 0.5 Hz filter), the ICA results are acceptable. However, high‐pass filters of up to 2 Hz cut‐off frequency should be used in mobile experiments, and more channels require a higher filter to reach an optimal decomposition. Fewer brain ICs were found in mobile experiments, but cleaning the data with ICA has been proved to be important and functional even with low‐density channel setups. Based on the results, we provide guidelines for different experimental settings that improve the ICA decomposition.TU Berlin, Open-Access-Mittel – 202

A Dynamical Systems Approach to Characterizing Brain–Body Interactions during Movement: Challenges, Interpretations, and Recommendations

Brain–body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of ‘brain’ activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task

EXAMINING FACE-SENSITIVE BRAIN POTENTIALS IN NATURAL ENVIRONMENTS USING MOBILE EEG

Abstract Faces are a unique type of stimulus for humans. As such, they are processed differently to other types of stimuli like houses or objects. In the past, laboratory based testing has been used to examine the neural correlates of human face processing. However, viewing faces in a laboratory differs considerably from how it occurs the real world. This thesis examines the neural mechanisms underlying the processing of face images in a naturalistic setting. A custom-design mobile brain and body imaging technique was used to explore the neural mechanisms governing naturalistic face processing. Simultaneous mobile electroencephalography (EEG) and eye-tracking data was recorded from participants while they freely viewed images presented in a mock art gallery. The synchronisation of both data streams allowed us to analyse the EEG signal by time-locking markers to naturally occurring visual events captured by the eye tracker. Using this methodology the effects of emotion, familiarity and body posture on the face-sensitive N170 ERP component were investigated. The findings demonstrated, for the first time, the possibility of detecting face-specific brain potentials in freely moving, unrestricted subjects during passive viewing of images, as well as during active interactions with another person. The results present the effects of emotional valence on the N170 amplitude and replicate previous lab-based findings. Furthermore, the effects of body posture on early visual ERPs, but not the face-sensitive N170 contribute new insights to the face processing literature. Finally, the N170 component produced during a dyadic social interaction is described in relation to previous laboratory based reports. The experimental chapters present a novel methodology for recording mobile EEG signals as well as an adaptable experimental design that can be used in a wide variety of fields. The thesis demonstrates that EEG activity associated to the viewing static faces in laboratory conditions resemble those produced in real world environments as well as during natural social interactions. Moreover, the effects of emotional content on face processing were represented in the N170 component particularly, when disgusted faces were viewed

Precursors and downstream consequences of prediction in language comprehension

During language comprehension, the brain rapidly integrates incoming linguistic stimuli to not only incrementally build a contextual representation, but also predict upcoming information. This predictive mechanism leads to behavioral facilitation of processing of expected words, as well as a reduction in amplitude of the N400, a neural response reflecting access of semantic memory. However, little research has identified a behavioral or neurophysiological cost of errors in prediction. Additionally, only recent work has begun to investigate neural activity related to prediction prior to encountering a predicted stimulus. Most work has focused on what happens immediately after a predicted or unpredicted stimulus is encountered. Here, I explore new avenues of research by examining downstream consequences of prediction during language comprehension on future recognition memory. Additionally, I test whether these consequences occur following any violation of predictions, or whether the semantic fit of the violation to the established context plays a role. Finally, I adapt a classic paradigm, word stem completion, to investigate electrophysiological activity following a cue that is modulated by how predictive the outcome is. With this work, I not only have discovered costs of failed and successful predictions and identified neural signals potentially related to generation of predictions, but also have researched prediction in novel ways that can continue to expand and further elucidate how this mechanism affects cognition and changes across populations

Development and applications of a smartphone-based mobile electroencephalography (EEG) system

Electroencephalography (EEG) is a clinical and research technique used to non-invasively acquire brain activity. EEG is performed using static systems in specialist laboratories where participant mobility is constrained. It is desirable to have EEG systems which enable acquisition of brain activity outside such settings. Mobile systems seek to reduce the constraining factors of EEG device and participant mobility to enable recordings in various environments but have had limited success due to various factors including low system specification. The main aim of this thesis was to design, build, test and validate a novel smartphone-based mobile EEG system.A literature review found that the term ‘mobile EEG’ has an ambiguous meaning as researchers have used it to describe many differing degrees of participant and device mobility. A novel categorisation of mobile EEG (CoME) scheme was derived from thirty published EEG studies which defined scores for participant and device mobilities, and system specifications. The CoME scheme was subsequently applied to generate a specification for the proposed mobile EEG system which had 24 channels, sampled at 24 bit at a rate of 250 Hz. Unique aspects of the EEG system were the introduction of a smartphone into the specification, along with the use of Wi-Fi for communications. The smartphone’s processing power was used to remotely control the EEG device so as to enable EEG data capture and storage as well as electrode impedance checking via the app. This was achieved by using the Unity game engine to code an app which provided the flexibility for future development possibilities with its multi-platform support.The prototype smartphone-based waist-mounted mobile EEG system (termed ‘io:bio’) was validated against a commercial FDA clinically approved mobile system (Micromed). The power spectral frequency, amplitude and area of alpha frequency waves were determined in participants with their eyes closed in various postures: lying, sitting, standing and standing with arms raised. Since a correlation analysis to compare two systems has interpretability problems, Bland and Altman plots were utilised with a priori justified limits of agreement to statistically assess the agreement between the two EEG systems. Overall, the results found similar agreements between the io:bio and Micromed systems indicating that the systems could be used interchangeably. Utilising the io:bio and Micromed systems in a walking configuration, led to contamination of EEG channels with artifacts thought to arise from movement and muscle-related sources, and electrode displacement.To enable an event related potential (ERP) capability of the EEG system, additional coding of the smartphone app was undertaken to provide stimulus delivery and associated data marking. Using the waist-mounted io:bio system, an auditory oddball paradigm was also coded into the app, and delivery of auditory tones (standard and deviant) to the participant (sitting posture) achieved via headphones connected to the smartphone. N100, N200 and P300 ERP components were recorded in participants sitting, and larger amplitudes were found for the deviant tones compared to the standard ones. In addition, when the paradigm was tested in individual participants during walking, movement-related artifacts impacted negatively upon the quality of the ERP components, although components were discernible in the grand mean ERP.The io:bio system was redesigned into a head-mounted configuration in an attempt to reduce EEG artifacts during participant walking. The initial approach taken to redesign the system involved using electronic components populated onto a flexible PCB proved to be non-robust. Instead, the rigid PCB form of the circuitry was taken from the io:bio waist-mounted system and placed onto the rear head section of the electrode cap via a bespoke cradle. Using this head-mounted system, in a preliminary auditory oddball paradigm study, ERP responses were obtained in participants whilst walking. Initial results indicate that artifacts are reduced in this head-mounted configuration, and N100, N200 and P300 components are clearly identifiable in some channels