Ensemble-based feature engineering mechanism to decode imagined speech from brain signals

Brain-computer interfaces (BCIs) attempt to establish a connection between the human mind and a computer system. While recent computational advances continue to improve these interfaces, human factors have been overlooked. Factors such as fatigue and attention play a key role in brain signal modulation. This arises the need for paradigms designed and implemented in terms of human factors. Therefore, it is proposed to improve the level of engagement to diminish fatigue and increase attention by a video game-based paradigm for an imagined speech BCI. For this purpose, a sample of 15 volunteers (females = 7) was recruited to study the quality of their imagined speech when it is evoked under an abstract scenario (traditional paradigm) and a video-game paradigm. This dataset helps to study the differences in imagined speech signals when using two different paradigms: (1) one that does not consider human factors, and (2) one that does. Additional applications may include designing imagined speech decoding models for BCI and studying the relationship between users' profile and their imagined speech signals.

Emotion classification using EEG (Electroencephalogram) brain signals has recently garnered significant interest due to its potential applications in fields such as mental health, human-computer interaction, and affective computing. The three prominent recurrent neural network (RNN) architectures GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and BiLSTM (Bidirectional Long Short-Term Memory) are thoroughly compared in this study with a focus on classifying emotions. The study examines how well these RNN variations capture temporal dependencies and patterns within EEG data to precisely discriminate between various emotional states. Based on classification accuracy and training effectiveness, these model's performance will be assessed. The results will reveal the most suitable architecture in terms of accuracy and robustness for EEG-based emotion classification. The findings provide valuable insights into choosing the appropriate RNN architecture for emotion classification tasks, advancing the understanding of AI's potential in interpreting human emotions through EEG signals.

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

Event Abstract Back to Event Analysis and classification of EEG signals for brain computer interfaces Ugur Halici1*, E. Agi1, C. Özgen1 and I. Ulusoy1 1 Computer Vision and Intelligent Systems Research Laboratory, Middle East Technical University, Turkey Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. Depending on the application, the device to be controlled might be a neuroprothesis, a wheel-chair or a computer. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. The neurophysiologic brain signals produced as results of these mental activities are collected by a data acquisition device and converted to digital form. Electroencephalogram (EEG) is the most common technique employed for data acquisition in BCI systems today, due to its time-space resolution, its price and its easiness in use when compared to the other acquisition techniques. The digital brain signals collected are processed for feature extraction and then classified in order to decide on the associated action. A control signal produced according to the decision is used to change the state of the device to be controlled. Providing a feedback signal informing the user about the new state of the controlled device completes the BCI cycle. The early BCI systems were designed to work on pre-defined neurophysiologic features, where a feedback produced by a fixed set-up was provided to the system users. In these early BCI systems, the users should learn by themselves how to produce neurophysiologic signals in order to control the system. In several studies, it is reported that weeks, even months may be needed in order the user to learn how to produce these signals by adaptation of their brain. Recently, BCI systems in which machine learning algorithms employed for adapting the BCI system by using user specific signals are started to emerge. In several studies, it is shown that the adaptation of the BCI systems in this way reduces drastically the time required the user to able to produce appropriate signals and adaptation time in terms of days, even minutes are reported [1-4] The aim of this study is to analyze EEG signals collected for BCI purposes by using techniques Principal Component Analysis, Fisher Linear Discriminant Analysis, and Independent Component Analysis in order to reduce the dimension of the feature vectors obtained by sampling EEG signals in an representative manner and then classify the low dimension feature vectors obtained in this way by using Neural Networks and Support Vector Machines, which are able to learn through samples. The experimental studies are still going on for feature extraction and classification. The experimental results obtained will be presented in the final paper for comparison purposes.

Enhancing the Social Impact of Contemporary Music with Neurotechnology

The modern commercial industry is increasingly dependent on the analysis of vast data records. Analysing big electroencephalogram (EEG) signal data (called brain signal data) plays an important role in a wide variety of applications such as healthcare practices, brain computer interface (BCI) systems, innovative education, privacy and security, and biometrics. The key objective of this paper is to establish a methodological framework for identifying communicative intentions of motor disabled people from EEG data for application in BCI systems. The proposed framework is designed based on common spatial pattern (CSP) data method and optimized ensemble (OE) machine learning method for the application of BCI technologies. The CSP method is used for discovering important features from EEG data and finally the extracted features are fed as an input to optimized ensemble (OE) method. The proposed method was tested on BCI Competition III dataset IVa, which contains motor imagery-based EEG signal data. The experimental results show that our proposed method can handle brain signal big data for identifying communicative intentions for an advanced BCI system. We compared the performance of our proposed method with several other existing methods. In comparison with other established methods, our method achieves higher classification accuracy performance. This research assists the experts in processing and analysing EEG signals for BCI applications. It also supports technologists to create a new EEG data analyser for BCI systems.KeywordsElectroencephalographyBrain computer interfaceCommon spatial patternMachine learning

As the capability of an electroencephalogram’s (EEG) measurement of the real-time electrodynamics of the human brain is known to all, signal processing techniques, particularly deep learning, could either provide a novel solution for learning but also optimize robust representations from EEG signals. Considering the limited data collection and inadequate concentration of during subjects testing, it becomes essential to obtain sufficient training data and useful features with a potential end-user of a brain–computer interface (BCI) system. In this paper, we combined a conditional variational auto-encoder network (CVAE) with a generative adversarial network (GAN) for learning latent representations from EEG brain signals. By updating the fine-tuned parameter fed into the resulting generative model, we could synthetize the EEG signal under a specific category. We employed an encoder network to obtain the distributed samples of the EEG signal, and applied an adversarial learning mechanism to continuous optimization of the parameters of the generator, discriminator and classifier. The CVAE was adopted to adjust the synthetics more approximately to the real sample class. Finally, we demonstrated our approach take advantages of both statistic and feature matching to make the training process converge faster and more stable and address the problem of small-scale datasets in deep learning applications for motor imagery tasks through data augmentation. The augmented training datasets produced by our proposed CVAE-GAN method significantly enhance the performance of MI-EEG recognition.

We present a novel integration of a brain-computer interface (BCI) with a multi-touch surface. BCIs based on the P300 paradigm often use a visual stimulus of a flashing character to elicit an event related potential in the brain's EEG signal. Traditionally, P300-based BCI paradigms use a grid layout of visual targets, commonly an alphabet, and allow users to select targets using their thoughts. In our new system a multi-touch table senses objects placed upon its surface and the system can highlight the objects on the table by flashing an area of light around them. This allows us to construct a P300-based BCI that uses a user-assembled collection of objects as targets, rather than a pre-determined grid layout. An experiment shows that our new paradigm works just as well as the traditional paradigms, thus highlighting the potential for BCIs to be integrated in a broader range of situations.

A brain-computer interface (BCI) can be defined as any system that can track the person's intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non-differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features - spatial and time-frequency properties - are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients.

Imagined speech is a process in which a person imagines words without saying them. Electroencephalogram (EEG)-based brain–computer interface (BCI) systems help in automatically identifying imagined speech to facilitate persons with severe brain disorders. Extracting meaningful information from the raw EEG signal is a challenging task due to the nonstationary nature of EEG signals. Decomposing a signal into several sub-bands (SBs) using rational dilation wavelet transform (RADWT) requires selecting predefined factual parameters, which is an arduous task. The main objective of this study is to propose an adaptive RADWT method capable of decomposing EEG signals by adaptively selecting the tuning parameters and classifying the EEG signals into distinct categories. The optimum tuning parameters of RADWT are obtained using particle swarm optimization (PSO) and used to decompose the EEG signals into several SBs. Several statistical features are elicited from each SB and used to input six different machine learning (ML) algorithms. This work uses a 64-channel EEG dataset recorded from 15 healthy people for three categories: long words, short words, and vowels. The performance of the proposed AISR system is evaluated using seven performance evaluation metrics: accuracy, recall, precision, Cohen’s kappa, F1-score, and area under the curve. The proposed system achieved the average classification accuracies of 87.26% ± 1.12%, 89.23% ± 0.95%, 95.5% ± 0.68%, and 92.16% ± 0.83% for long words, short–long words, short words, and vowels, respectively. When compared with the existing state-of-the-art, the proposed non-parametric decomposition approach and the Bagging algorithm achieved a 3%–5% improvement. The performance of the proposed method is validated using an open-access dataset.

ABSTRACT
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\nPutri, Anita Kurnia. 2020. “Analisis Kontrastif Adverbia Tinkatan Penanda Negasi dalam Bahasa Jepang dan Bahasa Jepang”. Thesis, department of Japanese Language and Culture, Faculty of Humanities, Diponegoro University. The Advisor: Lina Rosliana, S.S., M.Hum.
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\nThe problems that will be discussed in this thesis include: 1. What is the structure and meaning of the adverb in the level of negation markers in Japanese sentences? 2. What is the structure and meaning of the adverb at the level of the negation marker in Indonesian sentences? 3. How are the adverbs of the level of negation markers similar and different in Japanese and Indonesian sentences?
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\nMeanwhile, the purpose of this study is to describe the structure and meaning of adverbs in Japanese and Indonesian sentences. After knowing the structure and meaning, we will examine the similarities and differences in the structure and meaning of the adverbial levels of the negation markers of the two languages.
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\nIn analyzing the data, this study used the agih method followed by contrastive techniques. The data were obtained from the Japanese language website and news articles in Indonesian. Then the data is presented in words or descriptive.
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\nBased on the research, the structural similarities are both followed by verbs, adjectives. While the difference, adverbs in Japanese can be followed by phrases or clauses and negative forms, Indonesian adverbial followed by nouns. In meaning, the adverbs of the level of negation markers in Japanese and Indonesian indicates the meaning of the situation, the action is under adequate. And in terms of meaning it has 7 differences.
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\nKeywords : contrastive, adverbs, teido no fukushi
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Tactile feedback has the capability of reducing the workload on the visual channel, during visual feedback in brain-computer interfaces (BCIs). It is requisite to analyse the brain signals corresponding to the tactile stimulations. This work is aimed at analysing the brain signals while the users are vibrotactually stimulated. The brain signals are acquired non-invasively by electroencephalography (EEG), while brushless coin-type vibration motors are actuated in particular patterns to convey the object shape information on subjects' skin surface in form of vibrations. The acquired EEG signals are pre-processed to eliminate the effect of various types of noises and to extract the EEG signals corresponding to relevant frequency bands. Adaptive autoregressive (AAR) parameters are extracted from the pre-processed EEG signals and are finally classified by Naive Bayesian $(NB)$ approach, in order to recognize the vibratotactually stimulated object shapes from brain signals. In addition to the classifier output, subjects' verbal responses about the object shape they perceived are also noted for validation. Three successive sessions of shape recognition from vibrotactile pattern show an improvement in EEG classification accuracy from 63.75% to 74.37%, and also depicted learning of the stimulus from subjects' psychological response which is observed to increase from 75% to 95%. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increases the system efficacy.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes progressive loss of voluntary movement, including the ability to speak. As the disease rapidly progresses, there is a need for augmented and alternative communication that removes physical barriers required for engagement. Brain-Computer Interfaces (BCI) are a technology that allow for communication using only brain signals in response to visual stimuli. This project aimed to design an accessible and practical BCI for continuous, at-home use, and to increase BCI usability by using familiar facial stimuli to elicit a stronger brain response. The overall goal for enhancement of BCI usability is to maximize ease of use for individuals with ALS and their caregivers so that BCI technologies can make a useful impact and improve the everyday lives of people with ALS. In this project, a new miniaturized and low-cost P300 BCI system was designed using wireless and battery-operated EEG neuroimaging, modular software, and the use of an individualized BCI presentation stimuli. A 3-dimensional virtual environment platform was integrated as a navigational control output mechanism that executes commands received from the BCI to move a user's avatar in a virtual maze. Together, these form the virtual environment BCI (veBCI). The BCI stimuli presentation consisted of a custom approach of integrating different human face images with the aim of increasing user engagement and elicited brain activity, and therefore improving BCI online accuracy. The impact and effectiveness of the approach was tested via a verification BCI study in which 6 different stimuli conditions were utilized: male celebrity face, female celebrity face, male stranger face, female stranger face, a family member/close friend of the participant, and blank/no face as a control. A total of 16 volunteers (average age 56.5 and eight diagnosed with ALS) used the BCI system with all 6 face conditions in ecologically valid settings. Results indicate that this new miniaturized BCI is usable for both people with ALS and healthy controls. Significant differences existed in the amplitude of the P300 response in ALS and healthy controls. Additionally, differences between male and female participants demonstrated the potential role of personalization to optimize the visually evoked potential response and further increase BCI online accuracy. In conclusion, a home-use P300 BCI system was developed with a new generation of miniaturized, low-cost, and mobile neuroimaging running on an embedded computational platform. BCI stimuli with human face inclusion could help personalization of the BCI. Furthermore, virtual environments provide unique new ways to implement BCI output beyond typical spelling tasks and communication. Future work could focus on developing dedicated online signal processing to eliminate noise and improve online accuracy during home use.

Undoubtedly one of the most important strands of the brain-computer interface (BCI) method is an alternate communication method via brain signals. BCI converts electroencephalogram (EEG) signals from a perception of activity in the brain into user action utilising software and hardware. BCI has piqued the interest of researchers in a wide range of disciplines, such as cognitive science, deep learning, pattern matching, drug treatment medicine, etc. Patients suffering from neuro and cognitive disorders can be assisted through BCI, potentially enabling communication via gestures or just mental imagination. In this paper, a novel combination of Discrete Wavelet Transform (DWT) for extracting the best features and Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is adopted for classifying the EEG signals acquired during standing, walking and running on a treadmill. The dataset used is freely downloaded from Open Science Framework repository. The proposed DWT-LSTMRNN method delivers 96.7% accuracy while classifying four different signals, and thus has the potential to be investigated further on BCI competition datasets that will pave way for a real-time application.

Mental States are a function of brain activity; with advancements in Brain Computer Interface (BCI) tools, they can be effectively predicted. Generally, BCI researches are sophisticated requiring multi-channel electrodes, and often carried out in controlled lab environment. This paper illustrates that a simple BCI research, targeting specific region of brain, can be conducted with an elementary setup. A method is demonstrated to predict the level of attentiveness using Electroencephalography (EEG) signal obtained from single-channel, non-invasive, dry-electrode placed on prefrontal cortex region. The acquired raw signal was processed to obtain features which were used to classify the attention state into three classes. As Support Vector Machine (SVM) is more effective in training and classification of high dimensional data, it was implemented and its results were studied.