A MUSIC-based method for SSVEP signal processing

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100 %. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications

Review of real brain-controlled wheelchairs

This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented

A portable EEG-BCI framework enhanced by machine learning techniques

Brain Computer Interfaces (BCIs) allow direct communication between the human brain and external devices through the processing and interpretation of brain signals. Indeed, BCI represents the ultimate achievement in human-machine interaction, eliminating all the intermediate physical steps between the intention of an action and its implementation. Electroencephalography (EEG) plays a key role in BCIs being the least invasive technique for capturing brain electrical activity. However, high performance devices turn out to be uncomfortable and of unpractical use outside dedicated facilities, mainly due to the use of many electrodes. Conversely, single-channel EEG devices made of fewer electrodes provide weak and noisy signals difficult to interpret. In this PhD thesis, a portable BCI prototype enhanced by machine learning techniques for the classification of brain signals — and in particular of Steady State Visual Evoked Potentials (SSVEPs) — is proposed. The current study embraces the design, realization, characterization, and optimization of a BCI built on top of a cost-effective single-channel EEG device. The results have been validated both in offline and online sessions thanks to the collaboration of volunteers equipped with the given prototype. Due to its usability, the proposed framework may broaden the range of state-of-the-art BCI applications

A Novel Approach Of Independent Brain-computer Interface Based On SSVEP

Durante os últimos dez anos, as Interfaces Cérebro Computador (ICC) baseadas em Potenciais Evocados Visuais de Regime Permanente (SSVEP) têm chamado a atenção de muitos pesquisadores devido aos resultados promissores e as altas taxas de precisão atingidas. Este tipo de ICC permite que pessoas com dificuldades motoras severas possam se comunicar com o mundo exterior através da modulação da atenção visual a luzes piscantes com frequência determinada. Esta Tese de Doutorado tem o intuito de desenvolver um novo enfoque dentro das chamadas ICC Independentes, nas quais os usuários não necessitam executar tarefas neuromusculares para seleção visual de objetivos específicos, característica que a distingue das tradicionais ICCs-SSVEP. Assim, pessoas com difculdades motoras severas, como pessoas com Esclerose Lateral Amiotrófca (ELA), contam com uma nova alternativa de se comunicar através de sinais cerebrais. Diversas contribuições foram realizadas neste trabalho, como, por exemplo, melhoria do algoritmo extrator de características, denominado Índice de Sincronização Multivariável (ou MSI, do Inglês), para a detecção de potenciais evocados; desenvolvimento de um novo método de detecção de potenciais evocados através da correlação entre modelos multidimensionais (tensores); o desenvolvimento do primeiro estudo sobre a influência de estímulos coloridos na detecção de SSVEPs usando LEDs; a aplicação do conceito de Compressão na detecção de SSVEPs; e, fnalmente, o desenvolvimento de uma nova ICC independente que utiliza o enfoque de Percepção Fundo-Figura (ou FGP, do Inglês)

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio