scholarly journals Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

2020 ◽  
Vol 10 (22) ◽  
pp. 8075
Author(s):  
Itsaso Rodríguez-Moreno ◽  
José María Martínez-Otzeta ◽  
Basilio Sierra ◽  
Itziar Irigoien ◽  
Igor Rodriguez-Rodriguez ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Autonomous Navigation ◽  
Brain Computer Interface ◽  
Video Recording ◽  
Video Camera ◽  
Computer Interface ◽  
Important Research ◽  
New Approach ◽  
The Subject ◽  
Camera Surveillance

Video activity recognition, despite being an emerging task, has been the subject of important research due to the importance of its everyday applications. Video camera surveillance could benefit greatly from advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. In this paper, a new approach for video action recognition is presented. The new technique consists of introducing a method, which is usually used in Brain Computer Interface (BCI) for electroencephalography (EEG) systems, and adapting it to this problem. After describing the technique, achieved results are shown and a comparison with another method is carried out to analyze the performance of our new approach.

scholarly journals Video Activity Recognition: State-of-the-Art

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3160 ◽  
Author(s):  
Itsaso Rodríguez-Moreno ◽  
José María Martínez-Otzeta ◽  
Basilio Sierra ◽  
Igor Rodriguez ◽  
Ekaitz Jauregi
Keyword(s):  
Social Interaction ◽  
Activity Recognition ◽  
Autonomous Navigation ◽  
State Of The Art ◽  
Video Recording ◽  
Research Community ◽  
Important Research ◽  
Live Video ◽  
Video Cameras ◽  
The Subject

Video activity recognition, although being an emerging task, has been the subject of important research efforts due to the importance of its everyday applications. Surveillance by video cameras could benefit greatly by advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. The aim of this paper is to survey the state-of-the-art techniques for video activity recognition while at the same time mentioning other techniques used for the same task that the research community has known for several years. For each of the analyzed methods, its contribution over previous works and the proposed approach performance are discussed.

scholarly journals Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Robert Leeb ◽  
Doron Friedman ◽  
Gernot R. Müller-Putz ◽  
Reinhold Scherer ◽  
Mel Slater ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Beta Oscillations ◽  
Brain Waves ◽  
The Subject ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Spinal Cord Injured

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

Brain Computer Interface for Communication and Control of Peripherals and Appliances

2020 ◽  
Vol 17 (4) ◽  
pp. 1616-1621
Author(s):  
K. Sathish ◽  
Aritra Paul ◽  
Debapriya Roy ◽  
Ishmeet Kalra ◽  
Simran Bajaj
Keyword(s):  
Brain Computer Interface ◽  
Processing System ◽  
Audio Signal ◽  
Computer Interface ◽  
Single Frequency ◽  
Discrete Wavelet ◽  
Continuous Eeg ◽  
Transmission Reduction ◽  
The Subject ◽  
Simplified Solution

The concept is designed to improve upon the recent developed system, utilizing auditory steady state response (ASSR) as a basis for the Brain Computer Interface (BCI) paradigm. It utilizes the classification of signals through a discrete wavelet transform (DWT) before the actual transmission to reduce overhead at the processing system. The electroencephalogram (EEG) obtained from the subject is through a p300 based EEG receivers. A compression algorithm is used to reduce the bandwidth usage and provide a quicker transmission of the large and continuous EEG. An Arduino board along with a proximity sensor is used to detect the presence and distance of the subject and consequently control playback of a single frequency audio signal, which as received by the user, is used for producing the EEG signals. A continuous focus of the user is required on the playback of the single frequency sound to produce a sizeable reading. At the receiving end, another Arduino board is installed with an SD card module, which contains the commands, responsible for the actual control of the devices. The concept can be utilized for various purposes from controlling IoT based systems to wheelchairs and hospital beds as well as bionic limbs, which however are limited due to the overall bulk of all the equipment currently required. The main aim of this paper is to propose an improvement in the transmission, reduction the latency of the signals and to provide a concept for utilization by the handicapped or physically impaired patients. Since the EEG is obtained through the inner ear of the subject, it completely eliminates any need for invasive surgery and provides a simplified solution. Developments have shown to be able to achieve over 95% of accuracy in the domain, currently limited by length of the EEG required in order to process the actual commands from the subject’s brain.

scholarly journals A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Tao Geng ◽  
John Q. Gan ◽  
Matthew Dyson ◽  
Chun SL Tsui ◽  
Francisco Sepulveda
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Algorithms ◽  
Single Trial ◽  
Linear Discriminant ◽  
The Subject ◽  
Offline Analysis ◽  
Binary Classifiers ◽  
Novel Design ◽  
Mental Tasks

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

Channel Selection and Feature Extraction of ECoG-Based Brain-Computer Interface Using Band Power

2010 ◽  
Vol 44-47 ◽  
pp. 3564-3568 ◽  
Author(s):  
Hai Bin Zhao ◽  
Chong Liu ◽  
Chun Yang Yu ◽  
Hong Wang
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Channel Selection ◽  
Computer Interface ◽  
Fisher Linear Discriminant ◽  
Data Set ◽  
Linear Discriminant ◽  
Distinctive Features ◽  
The Subject ◽  
Components Analysis

Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and have used in brain-computer interface (BCI) research. This paper studies the channel selection and feature extraction using band powers (BP) for a typical ECoG-based BCI system. The subject images movement of left finger or tongue. Firstly, BP features were used for channel selection, and 11 channels which had distinctive features were selected from 64 channels. Then, the features of ECoG signals were extracted using BP, and the dimension of feature vector was reduced with principal components analysis (PCA). Finally, Fisher linear discriminant analysis (LDA) was used for classification. The results of the experiment showed that this algorithm has got good classification accuracy for the test data set.

HYBRID BRAIN–COMPUTER INTERFACE PARADIGM — A STUDY

2018 ◽  
Vol 30 (03) ◽  
pp. 1850022 ◽  
Author(s):  
Rajesh Singla
Keyword(s):  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Training Procedure ◽  
Information Transfer Rate ◽  
Activation Rate ◽  
The Subject ◽  
External Stimuli

The advancements in the field of brain–computer interface (BCI) are driven by the underlying motive of improving quality of life for both healthy as well as locked in subjects. Since BCI’s are based on the response of the human brain to training or external stimuli, the improvement in terms of performance can be achieved by either enhancing the subject training procedure or by improving the external stimuli to produce maximized event related potential (ERP). P300 and steady-state visually evoked potential (SSVEP) approaches have been the most common paradigms used for stimulus-based BCI’s world over. But recently, a large number of researchers are facing a problem of BCI illiteracy in subjects, where some of the subjects showed ineffective results while training with these BCI as independent stimuli. The concept of hybrid brain–computer interface (hBCI) is a step towards eradicating this problem. Our research deals with external stimuli-based ERP generation where we discuss and compare with experimentation, three different options of visual stimulus: conventional SSVEP stimulus, P300-SSVEP hybrid stimulus, distinct target colors for P300-SSVEP-based hybrid stimulus. This paper introduces a novel hBCI paradigm and discusses the validation of improved results by comparing with the already existing stimuli options. The parameters of comparison that were considered to validate our proposal were decision accuracy (Acc), information transfer rate (ITR) and false activation rate (FAR).

scholarly journals What It Takes to Be a Pioneer: Ability Expectations From Brain-Computer Interface Users

NanoEthics ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 227-239
Author(s):  
Johannes Kögel ◽  
Gregor Wolbring
Keyword(s):  
Brain Computer Interface ◽  
Pain Tolerance ◽  
The Body ◽  
Computer Interface ◽  
Physical Conditions ◽  
Computer Interfaces ◽  
Emotion Control ◽  
Wide Range ◽  
The Subject ◽  
High Level

AbstractBrain-computer interfaces (BCIs) are envisioned to enable new abilities of action. This potential can be fruitful in particular when it comes to restoring lost motion or communication abilities or to implementing new possibilities of action. However, BCIs do not come without presuppositions. Applying the concept of ability expectations to BCIs, a wide range of requirements on the side of the users becomes apparent. We examined these ability expectations by taking the example of therapeutic BCI users who got enrolled into BCI research studies due to particular physical conditions. Some of the expectations identified are quite explicit, like particular physical conditions and BCI “literacy”. Other expectations are more implicit, such as motivation, a high level of concentration, pain tolerance, emotion control and resources. These expectations may produce a conception of the human and a self-understanding among BCI users that objectify the body in favour of a brain-centred, cerebral notion of the subject which also plays its part in upholding a normality regime.

scholarly journals Brain–computer interface in the context of information retrieval systems in a library

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Magdalena Wójcik
Keyword(s):  
Information Retrieval ◽  
Information Science ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Library And Information Science ◽  
Content Type ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
The Subject ◽  
State Of Research

PurposeThe subject of this paper is the idea of Brain–Computer Interface (BCI). The main goal is to assess the potential impact of BCI on the design, use and evaluation of information retrieval systems operating in libraries.Design/methodology/approachThe method of literature review was used to establish the state of research. The search according to accepted queries was carried out in the Scopus database and complementary in Google Scholar. To determine the state of research on BCI on the basis of library and information science, a specialist LISTA abstract database was also searched. The most current papers published in the years 2015–2019 in the English language or having at least an abstract in this language were taken into account.FindingsThe analysis showed that BCI issues are extremely popular in subject literature from various fields, mainly computer science, but practically does not occur in the context of using this technology in information retrieval systems.Research limitations/implicationsDue to the fact that BCI solutions are not yet implemented in libraries and are rarely the subject of scientific considerations in the field of library and information science, this article is mainly based on literature from other disciplines. The goal was to consider how much BCI solutions can affect library information retrieval systems. The considerations presented in this article are theoretical in nature due to the lack of empirical materials on which to base. The author's assumption was to initiate a discussion about BCI on the basis of library and information science, not to propose final solutions.Practical implicationsThe results can be widely used in practice as a framework for the implementation of BCI in libraries.Social implicationsThe article can help to facilitate the debate on the role of implementing new technologies in libraries.Originality/valueThe problem of BCI is very rarely addressed in the subject literature in the field of library and information science.

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