Frontal alpha asymmetry refers to the difference between the right and left alpha activity over the frontal brain region. Increased activity in the left hemisphere has been linked to approach motivation and increased activity in the right hemisphere has been linked to avoidance or withdrawal. However, research on alpha asymmetry is diverse and has shown mixed results, which may partly be explained by the potency of the used stimuli to emotionally and motivationally engage participants. This review gives an overview of the types of affective stimuli utilized with the aim to identify which stimuli elicit a strong approach-avoidance effect in an affective context. We hope this contributes to better understanding of what is reflected by alpha asymmetry, and in what circumstances it may be an informative marker of emotional state. We systematically searched the literature for studies exploring event-related frontal alpha asymmetry in affective contexts. The search resulted in 61 papers, which were categorized in five stimulus categories that were expected to differ in their potency to engage participants: images & sounds, videos, real cues, games and other tasks. Studies were viewed with respect to the potency of the stimuli to evoke significant approach-avoidance effects on their own and in interaction with participant characteristics or condition. As expected, passively perceived stimuli that are multimodal or realistic, seem more potent to elicit alpha asymmetry than unimodal stimuli. Games, and other stimuli with a strong task-based component were expected to be relatively engaging but approach-avoidance effects did not seem to be much clearer than the studies using perception of videos and real cues. While multiple factors besides stimulus characteristics determine alpha asymmetry, and we did not identify a type of affective stimulus that induces alpha asymmetry highly consistently, our results indicate that strongly engaging, salient and/or personally relevant stimuli are important to induce an approach-avoidance effect.

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Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

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Physiological synchrony (PS) refers to the similarity in physiological responses of two or more individuals and may be an informative source of information in the field of affective computing. Up to now, PS has been assessed using either autonomic measures or neural measures. While in literature multiple physiological channels have already been combined into one composite index for PS assessment, multimodal PS, i.e., using a combination of autonomic and neural channels in a single composite index (‘A-N’ multimodal), has remained unexplored. A-N multimodal PS is promising for the robust detection of emotionally or cognitively relevant events, as both autonomic and neural activity are sensitive to these events. The aim of this study is (i) to review analytic approaches that have been used to combine multiple physiological channels into one composite index for PS, and (ii) to view these approaches in the light of their potential applicability to A-N multimodal PS. A literature search was conducted to find studies assessing PS based on a composite index of multiple autonomic channels or multiple channels in electroencephalographic (EEG) recordings. Four studies were found that assessed PS based on a composite index using multiple autonomic channels and 12 studies assessed PS based on a composite index using multiple EEG channels. We found that analytic approaches varied between studies. Some averaged over multiple channels after assessing PS separately per channel (N=4), or averaged over channels before assessing PS (N=1), while others used different linear combinations of channels based on spatio-spectral decomposition (N=1) or correlated component analysis (CCA,N=8). CCA finds linear combinations of channels that are maximally correlated between subjects and has up to now been used to assess neural PS. We suggest that this method may be most appropriate for the exploration of multimodal PS assessment.@en