Global optimal constrained ICA and its application in extraction of movement related cortical potentials from single-trial EEG signals

2018 ◽  
Vol 166 ◽  
pp. 155-169
Author(s):  
Elnaz Eilbeigi ◽  
Seyed Kamaledin Setarehdan
Keyword(s):  
Single Trial ◽  
Eeg Signals ◽  
Global Optimal ◽  
Constrained Ica ◽  
Cortical Potentials

scholarly journals A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3961
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina
Keyword(s):  
Recognition Performance ◽  
Low Complexity ◽  
Single Trial ◽  
Eeg Signals ◽  
Speed Increase ◽  
P300 Speller ◽  
Embedded Platform ◽  
Temporal Features ◽  
High Recognition Accuracy ◽  
Car Driving

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.

Sensitive Text Information Detection Based on Single-trial EEG Signals

2021 ◽  
Author(s):  
Huimin Li ◽  
Ying Zeng ◽  
Xiyu Song ◽  
Li Tong ◽  
Jun Shu ◽  
...  
Keyword(s):  
Single Trial ◽  
Eeg Signals ◽  
Text Information ◽  
Information Detection

A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials

2015 ◽  
Vol 126 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Ning Jiang ◽  
Leonardo Gizzi ◽  
Natalie Mrachacz-Kersting ◽  
Kim Dremstrup ◽  
Dario Farina
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Gait Initiation ◽  
Single Trial ◽  
Cortical Potentials
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