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This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS).

The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score.

The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p < 0.0001), theta beta ratio (TBR) is positively correlated with subjects’ RT (p < 0.0001), these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test), and 4) TBR and AGR can be effectively used to predict RBANS attention score.

Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation.

This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly.

To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.

DEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.

 The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.

Many of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.

It is generally accepted that massage can provide a lot of benefits to human health, especially for the brain functions. Little is known about the effect of unilateral massage on the brain activities. Nowadays, Swedish massage is a modern massage technique that is popular in both treatment and research fields. The purpose of this paper is to investigate the effect of unilateral Swedish massage on brain activities with electroencephalography (EEG) recording.

In total, 18 healthy adult participants (5 men, 13 women) aged between 22 and 36 years were massaged over one side of arm, forearm, hand, neck and face. Then the same procedures were repeated to another side of the body. EEG was recorded before (baseline) and during each massage condition. The absolute power of four common brain waves consisting of δ (0.5-4 Hz), θ (4-8 Hz), α (8-13 Hz), and β activities (13-30 Hz) from the quantitative EEG analysis between baseline and each massage condition were used to compare with the paired t-test.

The study found the reduction of δ and θ powers over bilateral frontal, fronto-central, and central areas. The increments of α power over the similar brain areas were also observed. These findings indicated the generalized effect of unilateral Swedish massage for inducing relaxation. Moreover, the significant reduction of β power was also found over right central area when left-arm massage was applied. This finding revealed the initial inhibitory effect of Swedish massage over right somatosensory cortex that received sensory stimulation through massage from left side of the body.

Unilateral Swedish massage induced the inhibitory effect at the contralateral somatosensory cortex and then produced the generalized effect which is compatible with relaxation.

To clarify the physiological and psychological effects of deep breathing, the effects of extreme prolongation of expiration breathing (Okinaga) were investigated using electroencephalogram (EEG) and electrocardiogram (ECG). Participants were five male Okinaga practitioners in their 50s and 60s. Participants performed Okinaga for 31 minutes while continuous EEG and ECG measurements were taken. After 16 minutes of Okinaga, and until the end of the session, the percentages of theta and alpha 2 waves were significantly higher than at baseline. After 20 minutes, and until the end of the session, the percentage of beta waves was significantly lower than at baseline. The high frequency component of heart rate variability was significantly lower after 12 minutes of Okinaga and lasted until 23 minutes. The low frequency/high frequency ratio was significantly lower after 18 minutes of Okinaga and until the end of the session. Okinaga produced relaxation, suggesting that deep breathing may relieve anxiety. However, study limitations include potential ambiguity in the interpretation of the low frequency/high frequency ratio, the small sample, and the fact that EEG was measured only on the forehead.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

This paper aims to examine a two-way interaction between social influencers’ number of followers (micro vs meso) and argument quality (weak vs strong) on consumers’ self-reported and brain responses to advertising posts on Instagram. Further, drawing upon source credibility theory and contemporary theories of persuasion, the Instagram users’ perceptions of the influencer’s credibility are predicted to mediate the hypothesized effects.

Through an online (N = 192) and a lab study (N = 112), the authors examined Instagram users’ responses to an advertising post from Instagram influencers in terms of perceived source credibility and electronic word-of-mouth intention, using validated multi-item scales from existing literatures and electroencephalogram (EEG) measures. The hypotheses were tested with a 2 (type of influencer: micro vs meso) × 2 (argument quality: weak vs strong) between-subject design using mediated moderated linear regression analysis.

The results highlight that meso-influencers are perceived as a credible source of information only when their product-related post provides strong argument quality. Moreover, this process involves an increase in users’ cognitive work (measured with EEG), with possible implications on marketing communication strategies and online message design.

The limitations of the work can serve as ideas for future research. First, this study did not account for the influencer’s relevance and resonance. Second, the authors studied consumer responses to online communication produced by Instagram influencers within a single product category. Another important product type distinction that requires further attention is between hedonic and utilitarian products. Finally, the two studies only used positive review content. Further research should study how consumers evaluate the source credibility of a micro- vs meso-influencer when they are exposed to negative reviews containing weak vs strong arguments.

The results suggest that marketers should carefully consider Instagram influencers based on the trade-offs between credibility and reach. Specifically, micro-influencers are perceived as more credible sources of information than meso-influencers, which means that they have greater potential to affect Instagram users’ behavior. Moreover, the results suggest that meso-influencers should leverage argument quality to enhance their credibility and draw greater positive outcomes for the products and brands they endorse.

To the best of the authors’ knowledge, this study is the first to investigate how the interaction between the type of social media influencer and the argument quality affects consumers’ self-reported and brain responses to advertising posts on Instagram. Moreover, using neuroscience, this study aims to shed light on the neurophysiological processes that drive consumer responses to product-related communication posted by different influencer types.

Schools have a significant role in ensuring children's wellbeing as children spend a lot of time at school. Students need to have an active role in their learning and an opportunity to participate in issues concerning wellbeing and studying. This research examines students' wellbeing in an innovative learning environment. The classroom is built with professionals, teachers and students (aged 9). The authors call it Learning Ground.

Students' wellbeing was measured with smart device application for a six-week period. Students answered the questionnaire with a Likert scale of five (very poor – excellent) responses. Two weeks during the six weeks research period, students were able to use digital study aids, EEG-biosensor headsets, to observe the effectiveness of their learning, defined by NeurSky app. The EEG-biosensors enabled students to use a tool to recognise their own learning factors during the lessons. The effectiveness was available to students via tablets all the time.

The students at the Learning Ground are satisfied with wellbeing and the environment support for students' wellbeing experience is notable. They have “good vibes” before and after the school day. When wearing EEG-headsets “study aids”, which enabled them to observe their learning via tablets at lessons, the wellbeing experience in the mornings even increased.

Schools need to be visionaries concerning 21st century learning and children's wellbeing. Building flexible learning environments and bringing innovative technologies into schools to provide active support for students will enable 21st century learning. Wellbeing of children should become first when developing the future schools.

Sleep disturbances, present in more than 90% of major depressive disorder (MDD) patients, are moderated by sex in adult MDD. In particular, slow-wave electroencephalographic activity (SWA; 0.5-4 Hz) accumulation is low and dissipation impaired. This SWA abnormality in depressed adult males does not change with age, suggesting that SWA abnormality appears at early ages. The present study evaluated sex differences in SWA in adolescents with MDD compared to healthy controls. We evaluated regularized sleep-wake schedules at home for 5-7 days, followed by two consecutive nights of sleep EEG recording. The study included 104 participants, 52 symptomatic and depressed subjects (MDD: 20 males and 32 females) and 52 healthy controls (HC: 20 males and 32 females), aged 13-18 years. SWA power and dissipation, and duration and latencies to each Non-Rapid Eye Movement (NREM) sleep period were calculated for each group. Results showed that SWA accumulation in the first NREM period was lower and its dissipation across the night more irregular in MDD males compared to HC males (P<0.009). By contrast, SWA was equivalent in MDD and HC females. In conclusion, as reported in adult MDD, the accumulation and dissipation of SWA was abnormal in depressed adolescents, but only in males. SWA abnormalities in adolescent MDD may relate to different depressive symptoms in females and males. These results underscore the need to develop sex-specific therapies to enhance and restore SWA in depressed adolescents.

The purpose of this study is to understand how neuroscientific tools are used and discussed in ongoing research on strategy in organizations.

The authors used a bibliometric study of bibliographic pairing to answer the research question. They collected data from the Web of Science and Scopus databases using the keywords “neuroscience*,” “neurostrategy*” and “neuroscientific*.”

This study presents a framework that relates fundamental aspects discussed in current research using neuroscientific tools: Neuroscience and its research tools in organizations; emotions and information processing; interdisciplinary application of neuroscientific tools; and moral and ethical influences in the leaders' decision-making process.

The inclusion of neuroscientific tools in Strategic Management research is still under development. There are criticisms and challenges related to the limitations and potential to support future research.

Despite recognizing the potential of neuroscientific tools in the mind and brain relationship, this study suggests that at this stage, because of criticisms and challenges, they should be used as support and in addition to other traditional research techniques to assess constructs and mechanisms related to strategic decisions and choices in organizations.

Neuroscientific methods in organizational studies can provide insights into individual reactions to ethical issues and raise challenging normative questions about the nature of moral responsibility, autonomy, intention and free will, offering multiple perspectives in the field of business ethics.

In addition to presenting the potential and challenges of using scientific tools in strategic management studies, this study helps create methodological paths for studies in strategic management.