Matthieu Duvinage

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.

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.

About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking

This paper presents a spectral and time-frequency analysis of EEG signals recorded on seven healthy subjects walking on a treadmill at three different speeds. An accelerometer was placed on the head of the subjects in order to record the shocks undergone by the EEG electrodes during walking. Our results indicate that up to 15 harmonics of the fundamental stepping frequency may pollute EEG signals, depending on the walking speed and also on the electrode location. This finding may call into question some conclusions drawn in previous EEG studies where low-delta band (especially around 1 Hz, the fundamental stepping frequency) had been announced as being the seat of angular and linear kinematics control of the lower limbs during walk. Additionally, our analysis reveals that EEG and accelerometer signals exhibit similar time-frequency properties, especially in frequency bands extending up to 150 Hz, suggesting that previous conclusions claiming the activation of high-gamma rhythms during walking may have been drawn on the basis of insufficiently cleaned EEG signals. Our results are put in perspective with recent EEG studies related to locomotion and extensively discussed in particular by focusing on the low-delta and high-gamma bands.

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.

Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.

Control of a lower limb active prosthesis with eye movement sequences

Current active leg prostheses do not integrate the most recent advances in Brain-Computer Interfaces (BCI) and bipedal robotics. Moreover, their actuators are seldom driven by the subjects intention. In this paper, we propose an original and biologically-inspired leg prosthesis control scheme, which brings together these three aspects. It is composed of an EOG-based eye tracker and a Programmable Central Pattern Generator (PCPG). In a first step, specific sequences of eye movements executed by the user are identified by the eye tracking system. These sequences are then converted to high-level commands (such as accelerate, decelerate or stop) and sent to the prosthesis actuator control unit. In this unit, a PCPG is implemented, which is able to model human walk in a perfectly periodic way. One of the main interests of that tool is the possibility to modify the gait pattern to adapt to different walking speeds in a smooth way. Several results from previous studies are summarized and discussed in order to demonstrate the feasibility of such a system.

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.

Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator

The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.