Implementation of SSVEP based BCI with Emotiv EPOC

Liu, Yue; Jiang, Xiaoliang; Cao, Tao; Wan, Feng; Mak, Peng Un; Vai, Mang I

doi:10.1109/vecims.2012.6273184

Cited by 106 publications

(62 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The EEG signals have a poor signal to noise ratio [19] which makes the brain activity patterns difficult to be detected. Nevertheless, it is not always possible to discriminate between the different mental tasks.…”

Section: Mec Methods Ameliorationmentioning

confidence: 99%

Anti-noise Capability Improvement of Minimum Energy Combination Method for SSVEP Detection

Trigui¹,

Zouch²,

Messaoud³

2016

ijacsa

View full text Add to dashboard Cite

Abstract-Minimum energy combination (MEC) is a widely used method for frequency recognition in steady state visual evoked potential based BCI systems. Although it can reach acceptable performances, this method remains sensitive to noise. This paper introduces a new technique for the improvement of the MEC method allowing ameliorating its Anti-noise capability. The Empirical mode decomposition (EMD) and the moving average filter were used to separate noise from relevant signals. The results show that the proposed BCI system has a higher accuracy than systems based on Canonical Correlation Analysis (CCA) or Multivariate Synchronization Index (MSI). In fact, the system achieves an average accuracy of about 99% using real data measured from five subjects by means of the EPOC EMOTIVE headset with three visual stimuli. Also by using four commands, the system accuracy reaches 91.78% with an information-transfer rate of about 27.18 bits/min.

show abstract

Section: Mec Methods Ameliorationmentioning

confidence: 99%

Anti-noise Capability Improvement of Minimum Energy Combination Method for SSVEP Detection

Trigui¹,

Zouch²,

Messaoud³

2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…One of the most consumer-oriented devices is the neuroheadset Emotiv Epoc+ (Liu et al, 2012;Duvinage et al, 2013), shown in (Figure 3A), which includes 14 electrodes distributed in the four lobules (Lang, 2012) electrode configuration shown ( Figure 3B). In this paper, we compare the Epoc+ with the actiCAP (V-Amp and Easy Cap) from Brain Products, see (Figure 3C) with electrode configuration shown in (Figure 3D).…”

Section: Selection Of the Bmi Systemmentioning

confidence: 99%

SOPHIA: Soft Orthotic Physiotherapy Hand Interactive Aid

McConnell

Vallejo

Moioli³

et al. 2017

Front. Mech. Eng.

View full text Add to dashboard Cite

This work describes the design, fabrication, and initial testing of a Soft Orthotic Physiotherapy Hand Interactive Aid (SOPHIA) for stroke rehabilitation. SOPHIA consists of (1) a soft robotic exoskeleton, (2) a microcontroller-based control system driven by a brain-machine interface (BMI), and (3) a sensorized glove for passive rehabilitation. In contrast to other rehabilitation devices, SOPHIA is the first modular prototype of a rehabilitation system that is capable of three tasks: aiding extension based assistive rehabilitation, monitoring patient exercises, and guiding passive rehabilitation. Our results show that this prototype of the device is capable of helping healthy subjects to open their hand. Finger extension is triggered by a command from the BMI, while using a variety of sensors to ensure a safe motion. All data gathered from the device will be used to guide further improvements to the prototype, aiming at developing specifications for the next generation device, which could be used in future clinical trials.

show abstract

“…However, ease of use and affordability come at the price of reduced signal quality. Still, a comparative analysis of SSVEP data acquired from EPOC and medical-grade EEG found that the data acquired from EPOC is reliable [32], although the authors cautioned that the Emotiv should not be used for medically serious cases [12]. The radio connection is also a limitation, but studies have shown its reliable use in realtime applications [22].…”

Section: State Of the Artmentioning

confidence: 99%

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Luca¹,

Karim

Mondada

2016

Swarm Intell

View full text Add to dashboard Cite

Search and rescue, autonomous construction, and many other semi-autonomous multi-robot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator's SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85% on some subjects, while the average over the ten subjects was 75%.

show abstract

Implementation of SSVEP based BCI with Emotiv EPOC

Cited by 106 publications

References 12 publications

Anti-noise Capability Improvement of Minimum Energy Combination Method for SSVEP Detection

Anti-noise Capability Improvement of Minimum Energy Combination Method for SSVEP Detection

SOPHIA: Soft Orthotic Physiotherapy Hand Interactive Aid

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Contact Info

Product

Resources

About