scholarly journals EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 6 ◽  
Author(s):  
Ying Zeng ◽  
Qunjian Wu ◽  
Kai Yang ◽  
Li Tong ◽  
Bin Yan ◽  
...  
Keyword(s):  
Rapid Serial Visual Presentation ◽  
Visual Presentation ◽  
Specific Model ◽  
Identity Authentication ◽  
Event Related Potential ◽  
Time Interval ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Biometric Trait ◽  
Over Time

Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face and EEG, together to evoke more specific and stable traits for authentication. The event-related potential (ERP) components induced by self-face and non-self-face (including familiar and not familiar) are investigated, and significant differences are found among different situations. On the basis of this, an authentication method based on Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) is proposed to build subject-specific model with optimized fewer channels. The accuracy and stability over time are evaluated to demonstrate the effectiveness and robustness of our method. The averaged authentication accuracy of 94.26% within 6 s can be achieved by our proposed method. For a 30-day averaged time interval, our method can still reach the averaged accuracy of 88.88%. Experimental results show that our proposed framework for EEG-based identity authentication is effective, robust, and stable over time.

Visual Imagery Classification Using Shapelets of EEG Signals

2012 ◽  
Author(s):  
Stewart Contreras ◽  
V. Sundararajan
Keyword(s):  
Time Series ◽  
Classification Scheme ◽  
Signal To Noise Ratio ◽  
Event Related Potential ◽  
Eeg Signals ◽  
Classification Rate ◽  
Average Classification Accuracy ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

The goal of this paper is to reconstruct three primitive shapes — rectangular cube, cone and cylinder — by analyzing electrical signals which are emitted by the brain. Three participants are asked to visualize these shapes. During visualization, a 14-channel neuroheadset is used to record electroencephalogram (EEG) signals along the scalp. The EEG recordings are then averaged to increase the signal to noise ratio which is referred to as an event related potential (ERP). Every possible subsequence of each ERP signal is analyzed in an attempt to determine a time series which is maximally representative of a particular class. These time series are referred to as shapelets and form the basis of our classification scheme. After implementing a voting technique for classification, an average classification accuracy of 60% is achieved. Compared to naive classification rate of 33%, we determine that the shapelets are in fact capturing features that are unique in the ERP representation of a unique class.

Backward masking during rapid serial visual presentation affects the amplitude but not the latency of the P3 event-related potential

2010 ◽  
Author(s):  
Benoit Brisson ◽  
Nicolas Robitaille ◽  
Alexandrine Deland-Bélanger ◽  
Thomas M. Spalek ◽  
Vincent Di Lollo ◽  
...  
Keyword(s):  
Rapid Serial Visual Presentation ◽  
Visual Presentation ◽  
Event Related Potential ◽  
Backward Masking

scholarly journals Fragile Memories for Fleeting Percepts

2021 ◽  
Author(s):  
Howard Bowman ◽  
Alberto Avilés
Keyword(s):  
Free Recall ◽  
Rapid Serial Visual Presentation ◽  
Recognition Performance ◽  
Visual Presentation ◽  
Recency Effect ◽  
Detection Performance ◽  
Conscious Perception ◽  
Perceptual Systems ◽  
The Brain

Our perceptual systems are exceptionally good at searching our sensory environments for salient stimuli. A key question is the extent to which this search is performed subliminally. We explore this using Rapid Serial Visual Presentation (RSVP), by comparing detection performance with the memory left for distractors, the stimuli that have to be rejected as non-targets in the process of searching for targets. Our findings are that “immediate” free recall of arbitrary distractors at RSVP rates is very poor, with a severe recency effect. Recognition performance was higher and less subject to recency, but still substantially lower than detection performance. We argue that these findings suggest that the brain subliminally searches for salient stimuli, and are also consistent with a theory we call the tokenized-percept hypothesis, which links conscious perception to the process of episodically marking experiences.

scholarly journals The Perceptual Neural Trace of Memorable Unseen Scenes

2018 ◽  
Author(s):  
Yalda Mohsenzadeh ◽  
Caitlin Mullin ◽  
Aude Oliva ◽  
Dimitrios Pantazis
Keyword(s):  
Time Scales ◽  
Temporal Resolution ◽  
Rapid Serial Visual Presentation ◽  
Visual Processing ◽  
Visual Presentation ◽  
High Temporal Resolution ◽  
Neural Signal ◽  
Subjective Awareness ◽  
Behavioral Studies ◽  
The Brain

ABSTRACTSome scenes are more memorable than others: they cement in minds with consistencies across observers and time scales. While memory mechanisms are traditionally associated with the end stages of perception, recent behavioral studies suggest that the features driving these memorability effects are extracted early on, and in an automatic fashion. This raises the question: is the neural signal of memorability detectable during early perceptual encoding phases of visual processing? Using the high temporal resolution of magnetoencephalography (MEG), during a rapid serial visual presentation (RSVP) task, we traced the neural temporal signature of memorability across the brain. We found an early and prolonged memorability related signal recruiting a network of regions in both dorsal and ventral streams, detected outside of the constraints of subjective awareness. This enhanced encoding could be the key to successful storage and recognition.

scholarly journals Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Chuncheng Zhang ◽  
Shuang Qiu ◽  
Shengpei Wang ◽  
Huiguang He
Keyword(s):  
Target Detection ◽  
Rapid Serial Visual Presentation ◽  
High Speed ◽  
Neural Control ◽  
Visual Presentation ◽  
Event Related Potential ◽  
New Method ◽  
Classification Method ◽  
Computer Interface ◽  
Natural Scene

Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain–computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm.Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target.Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment.Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Sign in / Sign up

Export Citation Format

Share Document