Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments

2021 ◽  
pp. 1-15
Author(s):  
Jie Hong ◽  
Xiansheng Qin
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
State Of The Art ◽  
Brain Computer Interface ◽  
Interface State ◽  
Computer Interface ◽  
Comprehensive Review ◽  
Recent Developments ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Over past two decades, steady-state evoked potentials (SSVEP)-based brain computer interface (BCI) systems have been extensively developed. As we all know, signal processing algorithms play an important role in this BCI. However, there is no comprehensive review of the latest development of signal processing algorithms for SSVEP-based BCI. By analyzing the papers published in authoritative journals in nearly five years, signal processing algorithms of preprocessing, feature extraction and classification modules are discussed in detail. In addition, other aspects existed in this BCI are mentioned. The following key problems are solved. (1) In recent years, which signal processing algorithms are frequently used in each module? (2) Which signal processing algorithms attract more attention in recent years? (3) Which modules are the key to signal processing in BCI field? This information is very important for choosing the appropriate algorithms, and can also be considered as a reference for further research. Simultaneously, we hope that this work can provide relevant BCI researchers with valuable information about the latest trends of signal processing algorithms for SSVEP-based BCI systems.

scholarly journals Embedded Brain Computer Interface: State-of-the-Art in Research

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4293
Author(s):  
Kais Belwafi ◽  
Sofien Gannouni ◽  
Hatim Aboalsamh
Keyword(s):  
State Of The Art ◽  
Brain Computer Interface ◽  
Experimental Studies ◽  
Interface State ◽  
Computer Interface ◽  
Excess Capacity ◽  
Motor Disabilities ◽  
Severe Motor ◽  
Signal Processing Algorithms ◽  
Available Technology

There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.

scholarly journals A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dheeraj Rathee ◽  
Haider Raza ◽  
Sujit Roy ◽  
Girijesh Prasad
Keyword(s):  
Scientific Community ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Word Generation ◽  
Machine Learning Methods ◽  
Computer Interfaces ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Healthy Participants

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

scholarly journals A review of the state of the art of feature extraction of electroencephalographic signals

2019 ◽  
pp. 21-27
Author(s):  
José Jaime Esqueda-Elizondo ◽  
Laura Jiménez-Beristáin ◽  
Carlos Alberto Chávez-Guzmán ◽  
Ricardo Jesús Renato Guerra-Fraustro
Keyword(s):  
Feature Extraction ◽  
State Of The Art ◽  
Brain Computer Interface ◽  
The State ◽  
Computer Interface ◽  
Work Team ◽  
Electroencephalographic Signals

We present a review of the state of the art of the techniques and algorithms most used in the selection and detection of characteristics of electroencephalographic signals of people when consciously performing activities. These features are numeric parameters that describe the behavior of the signal and are the basis of patterns. In addition, previous experiences in the acquisition of electroencephalographic signals using the Epoc brain-computer interface manufactured by Emotiv are presented. First, some techniques used to eliminate artifacts (disturbances) present in the signal generated by blinking, strong breathing or other movements that contaminate the signal are presented. Later, the algorithms most frequently used in the processing of electroencephalographic signals are shown for the extraction of characteristics that describe the behavior of these patterns and that can be used to detect and recognize patterns in other signals. Finally, we present the lessons that we have acquired as a work team in the recording of electroencephalographic signals in order to be helpful for beginners.

DNA Sequence Visualization

2011 ◽  
pp. 917-934
Author(s):  
Hsuan T. Chang
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Dna Sequence ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Graphical Representation ◽  
Visualization Process ◽  
Potential Applications ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

This chapter introduces various visualization (i.e., graphical representation) schemes of symbolic DNA sequences, which are basically represented by character strings in conventional sequence databases. Several visualization schemes are reviewed and their characterizations are summarized for comparison. Moreover, further potential applications based on the visualized sequences are discussed. By understanding the visualization process, the researchers will be able to analyze DNA sequences by designing signal processing algorithms for specific purposes such as sequence alignment, feature extraction, and sequence clustering, etc.

scholarly journals Critical issues in state-of-the-art brain–computer interface signal processing

2011 ◽  
Vol 8 (2) ◽  
pp. 025002 ◽  
Author(s):  
Dean J Krusienski ◽  
Moritz Grosse-Wentrup ◽  
Ferran Galán ◽  
Damien Coyle ◽  
Kai J Miller ◽  
...  
Keyword(s):  
Signal Processing ◽  
State Of The Art ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Critical Issues
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