Spelling Detection based on P300 Signal with Convolutional Neural Network (CNN) Algorithm

Brain Computer Interface (BCI) is a system that connects the human brain with the outside world for people who have motor skills disability problems. One form of utilization is the P300 speller which is used for character recognition or detection by classifying the P300 signal. The Convolutional Neural Network (CNN) method is a deep learning method that can be used to handle signal problems with ID-CNN. At the initial stage the data signal will be transformed and followed by a duplication process using RandomOverSampling because the amount of data in each class is not balanced. The data will be divided into training, validation, and test data. After that, a training with CNN will be conducted and followed by an evaluation to find the best model. The test results from this study are a good-fitting CNN model with an evaluation value consisting of an accuracy of 94.27%, precision of 90.64%, sensitivity / recall of 98.30%, and f-measure of 94.31%. Based on the test, the CNN method can be used and implemented in authentication detection based on the P300 signal

A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network

The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms. TSCNN automatically learns to extract EEG features in the two paradigms in the training process, and improves the decoding accuracy by 25.4% compared with the MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the versatility of TSCNN is verified as it provides considerable performance in both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios (95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world applications of EEG-based BCI systems

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.Comment: 10 page

Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum

The classification of sensorimotor rhythms in electroencephalography signals can enable paralyzed individuals, for example, to make yes/no decisions. In practice, these approaches are hard to implement due to the variability of electroencephalography signals between and within subjects. Therefore, we report a novel and fast machine learning model, meeting the need for efficiency and reliability as well as low calibration and training time. Our model extracts finely graded frequency bands from motor imagery electroencephalography data by using power spectral density and training a random forest algorithm for classification. The goal was to create a non-invasive generalizable method by training the algorithm with subject-independent EEG data. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets. With a balanced accuracy of 73.94%, our novel algorithm outperforms other state-of-the-art non-subject-dependent algorithms

Classification of Brain Signal (EEG) Induced by Shape-Analogous Letter Perception

Visual perception of English letters involves different underlying brain processes including brain activity alteration in multiple frequency bands. However, shape analogous letters elicit brain activities which are not obviously distinct and it is therefore difficult to differentiate those activities. In order to address discriminative feasibility and classification performance of the perception of shape-analogous letters, we performed an experiment in where EEG signals were obtained from 20 subjects while they were perceiving shape analogous letters (i.e., ‘p’, ‘q’, ‘b’, and ‘d’). Spectral power densities from five typical frequency bands (i.e., delta, theta, alpha, beta and gamma) were extracted as features, which were then classified by either individual widely-used classifiers, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA), or an ensemble of some of them. The F-score was employed to select most discriminative features so that the dimension of features was reduced. The results showed that the RF achieved the highest accuracy of 74.1% in the case of multi-class classification. In the case of binary classification, the best performance (Accuracy 86.39%) was achieved by the RF classifier in terms of average accuracy across all possible pairs of the letters. In addition, we employed decision fusion strategy to exert complementary strengths of different classifiers. The results demonstrated that the performance was elevated from 74.10% to 76.63% for the multi-class classification and from 86.39% to 88.08% for the binary class classification

A Brain-Controlled Exoskeleton with Cascaded Event-Related Desynchronization Classifiers

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the user’s intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The user’s mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol

Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis

Interfacce neurali: principio di funzionamento e recenti sviluppi

Un’interfaccia cervello-computer (dall’inglese brain-computer interface, BCI) è un sistema che acquisisce, elabora e decodifica le intenzioni dell'utente attraverso i suoi segnali cerebrali, convertendole in comandi utilizzabili per controllare un dispositivo artificiale, indipendentemente o solo parzialmente dai canali fisiologici di esecuzione costituiti dai muscoli e dai nervi. Il flusso di segnale dell'utente viene rilevato tramite sensori dedicati, subisce un processo di elaborazione e classificazione per estrarre le informazioni rilevanti, che a loro volta vengono utilizzate per generare una risposta sotto forma di feedback. La BCI è un'innovativa tecnologia che può essere impiegata in molteplici applicazioni, in particolare nel campo biomedico come supporto per le persone affette da paralisi, ma non va trascurata la sua potenziale utilità in altri ambiti. Questa tesi fornisce una panoramica sulla neurofisiologia, con un focus sull'anatomia della corteccia cerebrale e sui vari tipi di segnali neurali. Successivamente, vengono discusse le diverse tecniche utilizzate per registrare l'attività neurale, tra cui l'elettroencefalografia (EEG), la risonanza magnetica funzionale (fMRI) e la spettroscopia a infrarossi vicini (NIRS), nonché le interfacce neurali invasive. La tesi si addentra poi nelle Interfacce Cervello-Computer, discutendone i componenti, le classificazioni e i principi di funzionamento. I capitoli finali trattano le tecnologie azionate dal cervello, tra cui la comunicazione, il controllo del movimento e dell'ambiente e la neuroriabilitazione. L’elaborato si conclude con le prospettive future della ricerca sulle BCI. Nel complesso, lo scopo di questa tesi è fornire una panoramica completa sulla neurofisiologia, le tecniche di registrazione neurale, la tecnologia delle BCI e le tecnologie azionate dal cervello.A brain-computer interface (BCI) is a system that acquires, processes, and decodes the user's intentions through their brain signals, converting them into usable commands to control an artificial device, independently or only partially through physiological execution channels such as muscles and nerves. The user's signal flow is detected through dedicated sensors, undergoes a processing and classification process to extract relevant information, which in turn is used to generate a response in the form of feedback. BCI is an innovative technology that can be used in various applications, particularly in the biomedical field as a support for people with paralysis, but its potential usefulness in other areas should not be overlooked. This thesis provides an overview of neurophysiology, with a focus on the anatomy of the cerebral cortex and the various types of neural signals. Different techniques used to record neural activity are then discussed, including electroencephalography (EEG), functional magnetic resonance imaging (fMRI), near-infrared spectroscopy (NIRS), and invasive neural interfaces. The thesis then delves into Brain-Computer Interfaces, discussing their components, classifications, and operating principles. The final chapters cover brain-controlled technologies, including communication, movement and environmental control, and neurorehabilitation. The paper concludes with the future prospects of BCI research. Overall, the purpose of this thesis is to provide a comprehensive overview of neurophysiology, neural recording techniques, BCI technology, and brain-controlled technologies