scholarly journals EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1423 ◽  
Author(s):  
Natasha Padfield ◽  
Jaime Zabalza ◽  
Huimin Zhao ◽  
Valentin Masero ◽  
Jinchang Ren
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Motor Imagery ◽  
State Of The Art ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Processing Techniques ◽  
Imagery Techniques ◽  
Signal Processing Techniques

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces

2019 ◽  
Vol 57 (8) ◽  
pp. 1709-1725 ◽  
Author(s):  
Paula G. Rodrigues ◽  
Carlos A. Stefano Filho ◽  
Romis Attux ◽  
Gabriela Castellano ◽  
Diogo C. Soriano
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Motor Imagery ◽  
Space Time ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

2020 ◽  
pp. 2150003
Author(s):  
Pasquale Arpaia ◽  
Francesco Donnarumma ◽  
Antonio Esposito ◽  
Marco Parvis
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Binary Classification ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Selection For ◽  
A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

scholarly journals Accelerometer Based Method for Tire Load and Slip Angle Estimation

Vibration ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 174-186 ◽  
Author(s):  
Kanwar Bharat Singh ◽  
Saied Taheri
Keyword(s):  
Signal Processing ◽  
State Of The Art ◽  
Experimental Results ◽  
Slip Angle ◽  
Special Focus ◽  
Promising Technology ◽  
Research Activities ◽  
Novel Algorithms ◽  
Processing Techniques ◽  
Signal Processing Techniques

Tire mounted sensors are emerging as a promising technology, capable of providing information about important tire states. This paper presents a survey of the state-of-the-art in the field of smart tire technology, with a special focus on the different signal processing techniques proposed by researchers to estimate the tire load and slip angle using tire mounted accelerometers. Next, details about the research activities undertaken as part of this study to develop a smart tire are presented. Finally, novel algorithms for estimating the tire load and slip angle are presented. Experimental results demonstrate the effectiveness of the proposed algorithms.

Weak GNSS Signal Acquisition for Indoor Environments

2018 ◽  
pp. 485-514
Author(s):  
Seung-Hyun Kong
Keyword(s):  
Signal Processing ◽  
State Of The Art ◽  
High Sensitivity ◽  
Urban Canyon ◽  
Signal Acquisition ◽  
Indoor Environments ◽  
Weak Signals ◽  
Fast Acquisition ◽  
Processing Techniques ◽  
Signal Processing Techniques

High sensitivity and fast acquisition are two important goals that must be considered in the development of signal processing techniques for a GNSS acquisition function to meet the demands for LBS in GNSS-challenged environments, such as indoor and urban canyon. This chapter introduces the fundamentals of GNSS acquisition functions, GNSS acquisition techniques for new GNSS signals, and GNSS acquisition techniques achieving high sensitivity and fast acquisition. Therefore, this chapter contains useful information for engineers who study the fundamentals and principles of GNSS acquisition and the state-of-the-art GNSS signal acquisition techniques for weak signals.

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