EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers

EEG-neurofeedback and executive function enhancement in healthy adults: a systematic-review

EEG-neurofeedback training (EEG-NFT) is a promising technique that supports individuals in learning to modulate their brain activity to obtain cognitive and behavioural improvements. EEG-NFT is gaining increasing attention for its potential \u201cpeak performance\u201d applications on healthy individuals. However, evidence for clear cognitive performance enhancements with healthy adults is still lacking. In particular, whether EEG-NFT represents an effective technique for enhancing healthy adults\u2019 executive functions is still controversial. Therefore, the main objective of this systematic-review is to assess whether the existing EEG-NFT studies targeting executive functions have provided reliable evidence for NFT effectiveness. To this end, we conducted a qualitative analysis of the literature since the limited number of retrieved studies did not allow us meta-analytical comparisons. Moreover, a second aim was to identify optimal frequencies as NFT targets for specifically improving executive functions. Overall, our systematic review provides promising evidence for NFT effectiveness in boosting healthy adults\u2019 executive functions. However, more rigorous NFT studies are required in order to overcome the methodological weaknesses that we encountered in our qualitative analysis

Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi

Subject - specific - frequency - band for motor imagery EEG signal recognition based on common spatial spectral pattern

Over the last decade, processing of biomedical signals using machine learning algorithms has gained widespread attention. Amongst these, one of the most important signals is electroencephalography (EEG) signal that is used to monitor the brain activities. Brain-computer-interface (BCI) has also become a hot topic of research where EEG signals are usually acquired using non-invasive sensors. In this work, we propose a scheme based on common spatial spectral pattern (CSSP) and optimization of temporal filters for improved motor imagery (MI) EEG signal recognition. CSSP is proposed as it improves the spatial resolution while the temporal filter is optimized for each subject as the frequency band which contains most significant information varies amongst different subjects. The proposed scheme is evaluated using two publicly available datasets: BCI competition III dataset IVa and BCI competition IV dataset 1. The proposed scheme obtained promising results and outperformed other state-of-the-art methods. The findings of this work will be beneficial for developing improved BCI systems

A Game-based Neurofeedback Training System For Cognitive Rehabilitation in the Elderly

ABSTRACT There is a relationship between sustained attention and cognitive performance. The ability to sustain attention potentially leads to enhance cognitive functions. Attention Training provides a promising alternative therapy to enhance cognitive ability and it can be efficiently implemented with Neurofeedback Training (NFT) system. The purpose of this research is to develop a NFT system for attention training to enhance cognitive performance in older adults. The efficiency of the system was evaluated by establishing a clinical trial for healthy elderly and Mild Cognitive Impairment (MCI) patients to perform cognitive rehabilitation. There were totally 24 older adults, age 55-70 yrs, enrolled to this trial. Both healthy and MCI groups performed NFT interventions in 12 sessions, two sessions a week. The results showed it is possible to train older adults to modify their amplitude of certain brain wave ranges using a neurofeedback protocol targeting several wave ranges for cognitive performance improvement. Most participants succeeded to decrease the ratio of their theta/alpha power band in both groups. The relations between specific patterns of EEG activity and levels of cognitive performance can be investigated with the NFT system as a training technique. The system is aimed to encourage older adults to produce specific patterns of cortical activity in connection with an improved level of cognitive performance

A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment

Suwicha Jirayucharoensak,1,2 Pasin Israsena,1 Setha Pan-ngum,2 Solaphat Hemrungrojn,3 Michael Maes3 1Neural Signal Processing Research Team, Artificial Intelligence Research Unit, National Electronics and Computer Technology Center, Pathum Thani 12120, Thailand; 2Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand; 3Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, ThailandIntroduction: This study examines the clinical efficacy of a game-based neurofeedback training (NFT) system to enhance cognitive performance in patients with amnestic mild cognitive impairment (aMCI) and healthy elderly subjects. The NFT system includes five games designed to improve attention span and cognitive performance. The system estimates attention levels by investigating the power spectrum of Beta and Alpha bands.Methods: We recruited 65 women with aMCI and 54 healthy elderly women. All participants were treated with care as usual (CAU); 58 were treated with CAU + NFT (20 sessions of 30 minutes each, 2–3 sessions per week), 36 with CAU + exergame-based training, while 25 patients had only CAU. Cognitive functions were assessed using the Cambridge Neuropsychological Test Automated Battery both before and after treatment.Results: NFT significantly improved rapid visual processing and spatial working memory (SWM), including strategy, when compared with exergame training and no active treatment. aMCI was characterized by impairments in SWM (including strategy), pattern recognition memory, and delayed matching to samples.Conclusion: In conclusion, treatment with NFT improves sustained attention and SWM. Nevertheless, NFT had no significant effect on pattern recognition memory and short-term visual memory, which are the other hallmarks of aMCI. The NFT system used here may selectively improve sustained attention, strategy, and executive functions, but not other cognitive impairments, which characterize aMCI in women.Keywords: amnestic mild cognitive impairment, neurofeedback, cognition, executive functions, aging, serious gaming, brain–computer interfac