SSVEP-Based BCIs

This chapter describes the method of flickering targets, eliciting fundamental frequency changes in the EEG signal of the subject, used to drive machine commands after interpretation of user’s intentions. The steady-state response of the changes in the EEG caused by events such as visual stimulus applied to the subject via a computer screen is called steady-state visually evoked potential (SSVEP). This feature of the EEG signal can be used to form a basis of input to assistive devices for locked in patients to improve their quality of life, as well as for performance enhancing devices for healthy subjects. The contents of this chapter describe the SSVEP stimuli; feature extraction techniques, feature classification techniques and a few applications based on SSVEP based BCI

Influencing brain waves by evoked potentials as biometric approach: taking stock of the last six years of research

The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an electroencephalography (EEG), albeit with less potential than those offered by high-cost professional devices. Such devices make it possible for researchers a huge number of experiments that were once impossible in many areas due to the high costs of the necessary hardware. Many studies in the literature explore the use of EEG data as a biometric approach for people identification, but, unfortunately, it presents problems mainly related to the difficulty of extracting unique and stable patterns from users, despite the adoption of sophisticated techniques. An approach to face this problem is based on the evoked potentials (EPs), external stimuli applied during the EEG reading, a noninvasive technique used for many years in clinical routine, in combination with other diagnostic tests, to evaluate the electrical activity related to some areas of the brain and spinal cord to diagnose neurological disorders. In consideration of the growing number of works in the literature that combine the EEG and EP approaches for biometric purposes, this work aims to evaluate the practical feasibility of such approaches as reliable biometric instruments for user identification by surveying the state of the art of the last 6 years, also providing an overview of the elements and concepts related to this research area

Electrophysiological Studies of Visual Attention and of Emotion Regulation

Electrophysiological methods, such as electroencephalography (EEG) and electrocardiography (ECG), measure biological activity that allow us to infer underlying cognitive processes. In the first study, we use EEG to track feature-based attention (FBA), a form of visual attention that helps one detect objects with a particular color, motion, or orientation. We explore the use of SSVEPs, generated by flicker presented peripherally, to track attention in a visual search task presented centrally. Classification results show that one can track an observer’s attended color, which suggests that these methods may provide a viable means for tracking FBA in a real-time task. In the second study, we use cardiovascular measures to examine influences of the emotion regulation strategy of reappraisal. We examine cooperation and cardiovascular responses in individuals that were defected on by their opponent in the first round of an iterated Prisoner’s Dilemma. We find significant differences between the emotion regulation conditions using the biopsychosocial (BPS) model of challenge and threat, where participants primed with the reappraisal strategy were weakly comparable with a threat state of the BPS model and participants without an emotion regulation were weakly comparable with a challenge state of the BPS model. In the third study, we use EEG to study the chromatic sensitivity of FBA for color during a visual search task. We use SSVEP responses evoked through peripheral flicker to measure the spectral tuning of color detection mechanisms and how attentional selection is affected by distractor color. Our results find smaller responses for the distractor colors and suggest that feature-based attention to a particular color involves chromatic mechanisms that both enhance the response to a target and minimize responses to distractors

A supervised machine-learning method for detecting steady-state visually evoked potentials for use in brain computer interfaces: A comparative assessment

It is hypothesised that supervised machine learning on the estimated parameters output by a model for visually evoked potentials (VEPs), created by Kremlácek et al. (2002), could be used to classify steady-state visually evoked potentials (SSVEP) by frequency of stimulation. Classification of SSVEPs by stimulus frequency has application in SSVEP-based brain computer interfaces (BCI), where users are presented with flashing stimuli and user intent is decoded by identifying which stimulus the subject is attending to. We investigate the ability of the model of VEPs to fit the initial portions of SSVEPs, which are not yet in a steady state and contain characteristic features of VEPs superimposed with those of a steady state response. In this process the estimated parameters, as a function of the model for a given SSVEP response, were found. These estimated parameters were used to train several support vector machines (SVM) to classify the SSVEPs. Three initialisation conditions for the model are examined for their contribution to the goodness of fit and the subsequent classification accuracy, of the SVMs. It was found that the model was able to fit SSVEPs with a normalised root mean square error (NRMSE) of 27%, this performance did not match the expected NRMSE values of 13% reported by Kremlácek et al. (2002) for fits on VEPs. The fit data was assessed by the machine learning scheme and generated parameters which were classifiable by SVM above a random chance of 14% (Reang 9% to 28%). It was also shown that the selection of initial parameters had no distinct effect on the classification accuracy. Traditional classification approaches using spectral techniques such as Power Spectral Density Analysis (PSDA) and canonical correlation analysis (CCA) require a window period of data above 1 s to perform accurately enough for use in BCIs. The larger the window period of SSVEP data used the more the Information transfer rate (ITR) decreases. Undertaking a successful classification on only the initial 250 ms portions of SSVEP data would lead to an improved ITR and a BCI which is faster to use. Classification of each method was assessed at three SSVEP window periods (0.25, 0.5 and 1 s). Comparison of the three methods revealed that, on a whole CCA outperformed both the PSDA and SVM methods. While PSDA performance was in-line with that of the SVM method. All methods performed poorly at the window period of 0.25 s with an average accuracy converging on random chance - 14%. At the window period of 0.5 s the CCA only marginally outperformed the SVM method and at a time of 1 s the CCA method significantly (p<0.05) outperformed the SVM method. While the SVMs tended to improve with window period the results were not generally significant. It was found that certain SVMs (Representing a unique combination of subject, initial conditions and window period) achieved an accuracy as high as 30%. For a few instances the accuracy was comparable to the CCA method with a significance of 5%. While we were unable to predict which SVM would perform well for a given subject, it was demonstrated that with further refinement this novel method may produce results similar to or better than that of CCA

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

Standardisation and automatisation of the diagnosis of patients with disorders of consciousness: a machine learning approach applied to electrophysiological brain and body signals

Avances en la medicina moderna han llevado a un incremento en el número de pacientes diagnosticados con desordenes de consciencia (DOC). En estas condiciones, los pacientes se encuentran despiertos, pero no muestran signos de entendimiento acerca de si mismos o el lugar donde se encuentran. Una evaluación precisa de los pacientes tiene implicaciones medico-éticas y sociales, y es de suma importancia porque típicamente informa el pronóstico. Los diagnósticos erróneos, no obstante, es una gran preocupación en las clínicas debido a las limitaciones intrínsecas de las herramientas de diagnostico basados en comportamiento. Una tecnología accesible para asistir a los médicos es la electroencefalografía (EEG). In un estudio previo, introducimos el uso de marcadores extraídos de EEG en combinación con aprendizaje automático como una herramienta para el diagnostico de pacientes DOC. En este trabajo, desarrollamos una herramienta de análisis automatizado, y analizamos la aplicabilidad y limitaciones de este método. Adicionalmente, proponemos dos enfoques para incrementar la precision del diagnóstico: (1) el uso de múltiples modalidades de estimulación para incluir los correlatos neuronales de la integración multisensorial y (2) el análisis de las modulaciones de la actividad cardíaca mediadas por la conciencia. Nuestros resultados exceden el conocimiento actual en dos dimensiones. Clínicamente, encontramos que el método puede ser utilizada en contextos heterogéneos, confirmando la utilidad del aprendizaje automático como una herramientas para el diagnóstico clínico. Científicamente, nuestros resultados resaltan que las interacciones entre el cerebro y el cuerpo pueden ser el mecanismo fundamental para sostener la fusión de multiples sentidos en una única percepción, conduciendo a la emergencia de la consciencia. En conjunto, este trabajo ilustra la importancia del aprendizaje automático para la evaluación clínica individualizada, y crea un punto de partida para la inclusión de las funciones corporales en la cuantificación de los estados de conciencia globalesAdvances in modern medicine have led to an increase of patients diagnosed with disorders of consciousness (DOC). In these conditions, patients are awake, but without behavioural signs of awareness. An accurate evaluation of DOC patients has medicoethical and societal implications, and it is of crucial importance because it typically informs prognosis. Misdiagnosis of patients, however, is a major concern in clinics due to intrinsic limitations of behavioural tools. One accessible assisting methodology for clinicians is electroencephalography (EEG). In a previous study, we introduced the use of EEG-extracted markers and machine learning as a tool for the diagnosis of DOC patients. In this work, we developed an automated analysis tool, and analysed the applicability and limitations of this method. Additionally, we proposed two approaches to enhance the accuracy of this method: (1) the use of multiple stimulation modalities to include neural correlates of multisensory integration and (2) the analysis of consciousness-mediated modulations of cardiac activity. Our results exceed the current state of knowledge in two dimensions. Clinically, we found that the method can be used in heterogeneous contexts, confirming the utility of machine learning as an automated tool for clinical diagnosis. Scientifically, our results highlight that brain-body interactions might be the fundamental mechanism to support the fusion of multiple senses into a unique percept, leading to the emergence of consciousness. Taken together, this work illustrates the importance of machine learning to individualised clinical assessment, and paves the way for inclusion of bodily functions when quantifying global states of consciousness.Fil: Raimondo, Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Spatial Resolution of Local Field Potential Signals in Macaque V4

A main challenge for the development of cortical visual prostheses is to spatially localize individual spots of light, called phosphenes, by assigning appropriate stimulating parameters to implanted electrodes. Imitating the natural responses to phosphene-like stimuli at different positions can help in designing a systematic procedure to determine these parameters. The key characteristic of such a system is the ability to discriminate between responses to different positions in the visual field. While most previous prosthetic devices have targeted the primary visual cortex, the extrastriate cortex has the advantage of covering a large part of the visual field with a smaller amount of cortical tissue, providing the possibility of a more compact implant. Here, we studied how well ensembles of Multiunit activity (MUA) and Local Field Potentials (LFPs) responses from extrastriate cortical visual area V4 of a behaving macaque monkey can discriminate between two-dimensional spatial positions. We found that despite the large receptive field sizes in V4, the combined responses from multiple sites, whether MUA or LFP, has the capability for fine and coarse discrimination of positions. We identified a selection procedure that could significantly increase the discrimination performance while reducing the required number of electrodes. Analysis of noise correlation in MUA and LFP responses showed that noise correlations in LFP responses carry more information about the spatial positions. Overall, these findings suggest that spatial positions could be localized with patterned stimulation in extrastriate area V4

Error-related potentials for adaptive decoding and volitional control

Locked-in syndrome (LIS) is a condition characterized by total or near-total paralysis with preserved cognitive and somatosensory function. For the locked-in, brain-machine interfaces (BMI) provide a level of restored communication and interaction with the world, though this technology has not reached its fullest potential. Several streams of research explore improving BMI performance but very little attention has been given to the paradigms implemented and the resulting constraints imposed on the users. Learning new mental tasks, constant use of external stimuli, and high attentional and cognitive processing loads are common demands imposed by BMI. These paradigm constraints negatively affect BMI performance by locked-in patients. In an effort to develop simpler and more reliable BMI for those suffering from LIS, this dissertation explores using error-related potentials, the neural correlates of error awareness, as an access pathway for adaptive decoding and direct volitional control. In the first part of this thesis we characterize error-related local field potentials (eLFP) and implement a real-time decoder error detection (DED) system using eLFP while non-human primates controlled a saccade BMI. Our results show specific traits in the eLFP that bridge current knowledge of non-BMI evoked error-related potentials with error-potentials evoked during BMI control. Moreover, we successfully perform real-time DED via, to our knowledge, the first real-time LFP-based DED system integrated into an invasive BMI, demonstrating that error-based adaptive decoding can become a standard feature in BMI design. In the second part of this thesis, we focus on employing electroencephalography error-related potentials (ErrP) for direct volitional control. These signals were employed as an indicator of the user’s intentions under a closed-loop binary-choice robot reaching task. Although this approach is technically challenging, our results demonstrate that ErrP can be used for direct control via binary selection and, given the appropriate levels of task engagement and agency, single-trial closed-loop ErrP decoding is possible. Taken together, this work contributes to a deeper understanding of error-related potentials evoked during BMI control and opens new avenues of research for employing ErrP as a direct control signal for BMI. For the locked-in community, these advancements could foster the development of real-time intuitive brain-machine control