Towards ai-based interactive game intervention to monitor concentration levels in children with attention deficit

—Preliminary results to a new approach for neurocognitive training on academic engagement and monitoring of attention levels in children with learning difficulties is presented. Machine Learning (ML) techniques and a Brain-Computer Interface (BCI) are used to develop an interactive AI-based game for educational therapy to monitor the progress of children’s concentration levels during specific cognitive tasks. Our approach resorts to data acquisition of brainwaves of children using electroencephalography (EEG) to classify concentration levels through model calibration. The real-time brainwave patterns are inputs to our game interface to monitor concentration levels. When the concentration drops, the educational game can personalize to the user by changing the challenge of the training or providing some new visual or auditory stimuli to the user in order to reduce the attention loss. To understand concentration level patterns, we collected brainwave data from children at various primary schools in Brazil who have intellectual disabilities e.g. autism spectrum disorder and attention deficit hyperactivity disorder. Preliminary results show that we successfully benchmarked (96%) the brainwave patterns acquired by using various classical ML techniques. The result obtained through the automatic classification of brainwaves will be fundamental to further develop our full approach. Positive feedback from questionnaires was obtained for both, the AI-based game and the engagement and motivation during the training sessions

Cross-domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG

In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular wave) domains with both MLP and CNN methods. To achieve this, signals are measured from both the brain and forearm muscles and EMG data is gathered from a 4-class gesture classification experiment via the Myo Armband, and a 3-class mental state EEG dataset is acquired via the Muse EEG Headband. A hyperheuristic multi-objective evolutionary search method is used to find the best network hyperparameters. We then use this optimised topology of deep neural network to classify both EMG and EEG signals, attaining results of 84.76% and 62.37% accuracy, respectively. Next, when pre-trained weights from the EMG classification model are used for initial distribution rather than random weight initialisation for EEG classification, 93.82%(+29.95) accuracy is reached. When EEG pre-trained weights are used for initial weight distribution for EMG, 85.12% (+0.36) accuracy is achieved. When the EMG network attempts to classify EEG, it outperforms the EEG network even without any training (+30.25% to 82.39% at epoch 0), and similarly the EEG network attempting to classify EMG data outperforms the EMG network (+2.38% at epoch 0). All transfer networks achieve higher pre-training abilities, curves, and asymptotes, indicating that knowledge transfer is possible between the two signal domains. In a second experiment with CNN transfer learning, the same datasets are projected as 2D images and the same learning process is carried out. In the CNN experiment, EMG to EEG transfer learning is found to be successful but not vice-versa, although EEG to EMG transfer learning did exhibit a higher starting classification accuracy. The significance of this work is due to the successful transfer of ability between models trained on two different biological signal domains, reducing the need for building more computationally complex models in future research

Thumbs up, thumbs down:non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning

In this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of ’thumbs up’, ’thumbs down’, and ’relax’ in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a ‘20 Questions’ game