Mobile EEG and Tensor Approaches for Auditory Attention Analysis in Real-life
In recent years there has been considerable interest in recording neurophysiological information from humans in natural environments. With the emergence of high quality mobile EEG equipment, new EEG applications may be within reach. However, to date, the number of studies using true mobile EEG recordings in natural scenarios is surprisingly limited, which questions the feasibility of recording reliable EEG in out-of-the-lab scenarios. Moreover, the cognitive functioning of humans in real-life scenarios is likely to deviate from artificially created lab environments. With the advent of real-life mobile EEG applications and real-time signal processing, current methods need to be re-evaluated, and new aspects of the EEG acquisition should be addressed. The effects of distractions, changes in cognitive load, physical engagement and subject behavioral variability in real-life scenarios are hypothesized to influence neurophysiological brain responses as described in traditional confined EEG experiments.
This thesis seeks to address the feasibility of applying mobile EEG for research grade auditory attention experiments in real-life scenarios. Auditory attention is widely recognized as a very important concept that plays a vital role in the way humans process auditory information. It is inherently related to the user's current environment, making it a very relevant subject of study with mobile EEG outside a lab environment. We evaluated several aspects of EEG recording, analysis and interpretation that are of major importance for the application of mobile EEG. Specifically, we evaluate the response to acoustic stimuli in three-class auditory oddball and auditory attention detection (AAD) in natural speech paradigms. The former relies on event-related potentials (ERP) in the EEG in response to artificial stimuli, i.e. P300, which is one of the most studied potentials in EEG, predominantly for brain-computer-interfaces (BCI). In contrast, AAD is based on tracking cortical EEG responses, in relation to attended natural speech, which holds potential for application in assistive devices such as hearing aids. The usage of regular speech stimuli strengthens the natural character of our experimentation.
The first part of this thesis focuses on the signal analysis in three-class auditory oddball paradigms. We introduce the concepts of canonical polyadic decompositions (CPD), and decompositions in multi linear (Lr,Lr,1) terms (LL1) of higher-order EEG data. We demonstrate their effectiveness in decomposing EEG datasets in a data-driven way, to obtain relevant components related to the P300. Additionally, we show that it is possible to eliminate the explicit subject-dependent calibration phase with a tensor-based decomposition (CPD/LL1) augmented with non-subject-specific templates, without sacrificing classification accuracy. This allows for instantaneous classification results that, on average, are similar to those of the subject-specific trained models. These tensor approaches lend themselves for use as data-driven classification methods of EEG that could conceivably lead to faster usage of BCI systems and provide meaningful information of the subject's performance from the mobile EEG in a more natural way.
Besides classification, we gained considerable insight with regard to the factors in real-life recordings that influence the neurophysiological responses such as the P300. We evaluate the ERP and single-trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenarios while pedaling on a fixed bike or cycling around freely. In addition, we also carefully evaluate the trial-specific motion artifacts through independent gyroscope measurements and control for muscle artifacts. This work was the first to successfully examine such aspects simultaneously in one study. Our findings suggest that cognitive paradigms measured in natural real-life scenarios are influenced significantly by increased cognitive load due to being in an unconstrained environment. Furthermore, our study paved the way for other free cycling studies; very recently our results were replicated by others. All in all, these results have strengthened our conviction that the lack of subject response is often the bottleneck in active BCIs and the attentional efforts of the subject need to be carefully evaluated.
In the last part we address the conscious attentional efforts in more realistic scenarios. To this end we evaluate mobile EEG recordings at-home for learning in an auditory context. We describe a closed-loop online analysis of AAD applied to natural speech in a cocktail party scenario. In addition, the effects of personalized training via neurofeedback are investigated. We conducted two experiments that took place in an office and home environment. The results prove the feasibility of AAD outside the lab, which is promising for future applications such as in auditory assistive devices. Moreover, the high variability between subjects in physiological responses as recorded with the EEG, highlight the importance of considering EEG training to increase the efficiency of the AAD. Preliminary evidence regarding changes in AAD performance during training was obtained and future studies are needed to examine these effects in more detail. Finally, this work suggests that multiple modalities, e.g. behavioral, physical and neurophysiological, need to be considered when evaluating users' cognitive performance exhaustively in real-life situations.
To conclude, even though our investigations have only touched upon a limited section of the wide variety of neurophysiological processes, our results demonstrate the feasibility of truly mobile EEG applications. The prospect of being able to achieve (online) application of the auditory oddball and AAD in out-of-the-lab experiments, serves as a continuous incentive for future research. Furthermore, our results encourage future mobile EEG studies to consider a holistic approach in order to extend, in the best possible way, the current lab-based knowledge of cognitive brain monitoring to real-life scenarios.