Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods

The evolution of AI approaches for motor imagery EEG-based BCIs

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.Comment: Submitted to Italian Workshop on Artificial Intelligence for Human Machine Interaction (AIxHMI 2022), December 02, 2022, Udine, Ital

A magnetoencephalography dataset for motor and cognitive imagery-based brain–computer interface

However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of a novel pattern recognition machin

Deep Neural Networks based Meta-Learning for Network Intrusion Detection

The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework; INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks.Comment: Pages: 15, Figures: 10 and Tables:

Brain wave classification using long short - term memory based OPTICAL predictor

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL

Emprego de Banco de Filtros e do Teorema de Imersão de Takens em Padrões Espaciais para a Classificação de Imagética Motora em Interfaces Cérebro-Computador

As Interfaces Cérebro-Computador (BCI) são sistemas que provêm uma alternativa para que pessoas com perda severa ou total do controle motor possam inte- ragir com o ambiente externo. Para mapear intenções individuais em operações de má- quina, os sistemas de BCI empregam um conjunto de etapas que envolvem a captura e pré-processamento dos sinais cerebrais, a extração e seleção de suas características mais relevantes e a classificação das intenções. Neste trabalho, diferentes abordagens para a extração de características de sinais cerebrais foram avaliadas, a mencionar: i) Padrões Espectro-Espaciais Comuns (CSSP); ii) Padrões Esparsos Espectro-Espaciais Comuns (CSSSP); iii) CSSP com banco de filtros (FBCSSP); e, finalmente, iv) CSSSP com banco de filtros (FBCSSSP). Em comum, essas técnicas utilizam de filtragem de banda de frequências e reconstrução de espaços para ressaltar similaridades entre si- nais. A técnica de Seleção de Características baseada em Informação Mútua (MIFS) foi adotada para a redução de dimensionalidade das características extraídas e, em se- guida, Máquinas de Vetores de Suporte (SVM) foram utilizadas para a classificação do espaço de exemplos. Os experimentos consideraram o conjunto de dados BCI Compe- tition IV-2b, o qual conta com sinais produzidos pelos eletrodos nas posições C3, Cz e C4 a fim de identificar as intenções de movimentação das mãos direita e esquerda. Conclui-se, a partir dos índices kappa obtidos, que os extratores de características adotados podem apresentar resultados equiparáveis ao estado da arte.

Transfer Learning for Motor Imagery based Brain Computer Interfaces

Current electroencephalogram (EEG) based brain-computer interface (BCI) systems have limited real-world practicality due to a number of issues, including the long calibration period required before each use. This thesis focuses on reducing the time required to calibrate the BCI system without sacrificing classification accuracy. To address this issue, previously collected EEG data could be potentially mined and reused in calibrating the BCI model for a new user/session. However, this is not a trivial task due to two key challenges. First, there are considerable non-stationarities between the current and previously collected EEG signals. Secondly, due to between-session variations, not all the previously collected EEG signals are helpful in training the new BCI model. Initially, the thesis explored the application of distribution alignment techniques to reduce the effects of EEG non-stationarity. A novel multiclass data space alignment (MDSA) algorithm was proposed and evaluated. Our results showed that the proposed MDSA alignment algorithm successfully improved the classification accuracy and reduced the effects of non-stationarity. The thesis then addressed the second challenge by developing a new framework. This framework utilised a new algorithm that identifies whether or not the new session would benefit from transfer learning. If so, a novel similarity measurement, called the Jensen-Shannon Ratio (JSR), was proposed to select one of the past session for training the BCI model. The proposed framework outperformed state-of-the-art algorithms when there were as few as five labelled trials per class available from the new session. Despite success to some extent the proposed framework was limited to a binary selection between only one of the past sessions and current data for training the BCI model. Finally, the thesis utilised the findings of the previous research in order to address both challenges. A novel transfer learning framework was proposed for long-term BCI users. The proposed framework utilised regularisation, alignment and weighting to train a BCI which outperformed state-of-the-art algorithms even when only two trials per class from the new session were available