Thomas Hoellinger

Performance of the Emotiv Epoc headset for P300-based applications

BackgroundFor two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer’s needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available.MethodsThis paper aims at statistically comparing a medical-grade system, the ANT device, and the Emotiv Epoc headset by determining their respective performances in a P300 BCI using the same electrodes. On top of that, a review of previous Emotiv studies and a discussion on practical considerations regarding both systems are proposed. Nine healthy subjects participated in this experiment during which the ANT and the Emotiv systems are used in two different conditions: sitting on a chair and walking on a treadmill at constant speed.ResultsThe Emotiv headset performs significantly worse than the medical device; observed effect sizes vary from medium to large. The Emotiv headset has higher relative operational and maintenance costs than its medical-grade competitor.ConclusionsAlthough this low-cost headset is able to record EEG data in a satisfying manner, it should only be chosen for non critical applications such as games, communication systems, etc. For rehabilitation or prosthesis control, this lack of reliability may lead to serious consequences. For research purposes, the medical system should be chosen except if a lot of trials are available or when the Signal-to-Noise Ratio is high. This also suggests that the design of a specific low-cost EEG recording system for critical applications and research is still required.

Design and Control of the MINDWALKER Exoskeleton

Powered exoskeletons can empower paraplegics to stand and walk. Actively controlled hip ab/adduction (HAA) is needed for weight shift and for lateral foot placement to support dynamic balance control and to counteract disturbances in the frontal plane. Here, we describe the design, control, and preliminary evaluation of a novel exoskeleton, MINDWALKER. Besides powered hip flexion/extension and knee flexion/extension, it also has powered HAA. Each of the powered joints has a series elastic actuator, which can deliver 100 Nm torque and 1 kW power. A finite-state machine based controller provides gait assistance in both the sagittal and frontal planes. State transitions, such as stepping, can be triggered by the displacement of the Center of Mass (CoM). A novel step-width adaptation algorithm was proposed to stabilize lateral balance. We tested this exoskeleton on both healthy subjects and paraplegics. Experimental results showed that all users could successfully trigger steps by CoM displacement. The step-width adaptation algorithm could actively counteract disturbances, such as pushes. With the current implementations, stable walking without crutches has been achieved for healthy subjects but not yet for SCI paraplegics. More research and development is needed to improve the gait stability.

A P300-based Quantitative Comparison between the Emotiv Epoc Headset and a Medical EEG Device

EEG-based systems have been the most widely used in the field of Brain-Computer Interfaces (BCI) for two decades. Plenty of applications have been proposed from games to rehabilitation systems. Until recently, EEG recording devices were too expensive for an end-user. Today, several low-cost alternatives have appeared on the market. The most sophisticated of these low-cost devices is the Emotiv Epoc headset. Some studies reported that this device is suitable for customers in terms of performance. However, none of the previous studies reported to what extent the Emotiv headset is working well compared to a medical system. The aim of this paper is thus to scientifically compare a medical system and the Emotiv Epoc headset by determining their respective performances in the context of a P300 BCI paradigm. In this study, seven healthy subjects performed P300 experiments and two different conditions were studied: sitting on a chair and walking on a treadmill at constant speed. Results show that the Emotiv headset, although able to record EEG data and not only artifacts, is sometimes significantly worse than a medical system. Those results suggest that the design of a specific low-cost EEG recording systems for rehabilitation purposes at a low price is still required.

From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

EMG patterns during assisted walking in the exoskeleton

Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts, and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Optimizing the Performances of a P300-Based Brain–Computer Interface in Ambulatory Conditions

Brain-computer interfaces (BCIs) enable their users to interact with their surrounding environment using the activity of their brain only, without activating any muscle. This technology provides severely disabled people with an alternative mean to communicate or control any electric device. On the other hand, BCI applications are more and more dedicated to healthier people, with the aim of giving them access to augmented reality or new rehabilitation tools. As it is noninvasive, light and relatively cheap, electroencephalography (EEG) is the most used acquisition technique to record cerebral activity of the BCI users. However, when using such type of BCI, user movements are likely to provoke motions of the measuring electrodes which can severely damage the EEG quality. Thus, current BCI technology requires that the user sits and performs as little movements as possible. This is of course a strong limitation of BCI for use in ordinary life. Very recently, preliminary studies have been published in the literature and suggest that BCI applications can be realized even in the physically moving context. In this paper, we thoroughly investigate the possibility to develop a P300-based BCI system in ambulatory condition. The study is based on experimental data recorded with seven subjects executing a visual P300 speller-like discrimination task while simultaneously walking at different speeds on a treadmill. It is demonstrated that a P300-based BCI is definitely feasible in such conditions. Different artifact correction methods are described and discussed in detail. To conclude, a recommended approach is given for the development of a real-time application.

A five-state P300-based foot lifter orthosis: Proof of concept

Current lower limb prostheses do not integrate recent developments in robotics and in Brain-Computer Interfaces (BCIs). In fact, active lower limb prostheses seldom consider the users intent, they often determine the correct movement from those of healthy parts of the body or from the residual limb. Recently, an emerging idea for non-invasive BCIs was proposed to allow such low bitrate systems to control a lower limb prosthesis thanks to a Central Pattern Generator (CPG) widely used in robotics. This CPG allows to automatically generate a periodic gait pattern. Furthermore, the CPG pattern frequency and magnitude can be adapted according to the specific gait behavior of the patient and his desired speed. This paper proves the concept of combining a human gait model based on a CPG and a classic but non-natural P300 BCI in order to consider the users intent. The details of how the entire chain can be practically implemented are given. Finally, preliminary results on four healthy subjects for a four-speed P300-based lower limb orthosis with a non-control state are presented. Globally, results are satisfying and prove the feasibility of such systems.

Oscillations in the human brain during walking execution, imagination and observation

Gait is an essential human activity which organizes many functional and cognitive behaviors. The biomechanical constraints of bipedalism implicating a permanent control of balance during gait are taken into account by a complex dialog between the cortical, subcortical and spinal networks. This networking is largely based on oscillatory coding, including changes in spectral power and phase-locking of ongoing neural activity in theta, alpha, beta and gamma frequency bands. This coding is specifically modulated in actual gait execution and representation, as well as in contexts of gait observation or imagination. A main challenge in integrative neuroscience oscillatory activity analysis is to disentangle the brain oscillations devoted to gait control. In addition to neuroimaging approaches, which have highlighted the structural components of an extended network, dynamic high-density EEG gives non-invasive access to functioning of this network. Here we revisit the neurophysiological foundations of behavior-related EEG in the light of current neuropsychological theoretic frameworks. We review different EEG rhythms emerging in the most informative paradigms relating to human gait and implications for rehabilitation strategies.

A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes

Recent research has shown that a P300 system can be used while walking without requiring any specific gait-related artifact removal techniques. Also, standard EEG-based Brain-Computer Interfaces (BCI) have not been really assessed for lower limb rehabilitation/prosthesis. Therefore, this paper gives a first baseline estimation (for future BCI comparisons) of the subjective and objective performances of a four-state P300 BCI plus a non-control state for lower-limb rehabilitation purposes. To assess usability and workload, the System Usability Scale and the NASA Task Load Index questionnaires were administered to five healthy subjects after performing a real-time treadmill speed control. Results show that the P300 BCI approach could suit fitness and rehabilitation applications, whereas prosthesis control, which suffers from a low reactivity, appears too sensitive for risky and crowded areas.