Implantable Brain Computer Interface Devices Based on Mems Technology

2021 ◽  
Author(s):  
Long-Chun Wang ◽  
Zhe-Jun Guo ◽  
Ye Xi ◽  
Ming-Hao Wang ◽  
Bo-Wen Ji ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Mems Technology ◽  
Interface Devices

scholarly journals Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 384 ◽  
Author(s):  
Roberto Sánchez-Reolid ◽  
Arturo García ◽  
Miguel Vicente-Querol ◽  
Luz Fernández-Aguilar ◽  
María López ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Application Programming Interface ◽  
Computer Interface ◽  
Emotional States ◽  
Artificial Neural ◽  
Highly Correlated ◽  
Interface Devices

Estimation of human emotions plays an important role in the development of modern brain-computer interface devices like the Emotiv EPOC+ headset. In this paper, we present an experiment to assess the classification accuracy of the emotional states provided by the headset’s application programming interface (API). In this experiment, several sets of images selected from the International Affective Picture System (IAPS) dataset are shown to sixteen participants wearing the headset. Firstly, the participants’ responses in form of a self-assessment manikin questionnaire to the emotions elicited are compared with the validated IAPS predefined valence, arousal and dominance values. After statistically demonstrating that the responses are highly correlated with the IAPS values, several artificial neural networks (ANNs) based on the multilayer perceptron architecture are tested to calculate the classification accuracy of the Emotiv EPOC+ API emotional outcomes. The best result is obtained for an ANN configuration with three hidden layers, and 30, 8 and 3 neurons for layers 1, 2 and 3, respectively. This configuration offers 85% classification accuracy, which means that the emotional estimation provided by the headset can be used with high confidence in real-time applications that are based on users’ emotional states. Thus the emotional states given by the headset’s API may be used with no further processing of the electroencephalogram signals acquired from the scalp, which would add a level of difficulty.

scholarly journals Brain–computer interface devices for patients with paralysis and amputation: a meeting report

2016 ◽  
Vol 13 (2) ◽  
pp. 023001 ◽  
Author(s):  
K Bowsher ◽  
E F Civillico ◽  
J Coburn ◽  
J Collinger ◽  
J L Contreras-Vidal ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Meeting Report ◽  
Interface Devices

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information
Sign in / Sign up

Export Citation Format

Share Document