131 research outputs found
Steady-State movement related potentials for brain–computer interfacing
An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible
Crowd Modeling and Control via Cooperative Adaptive Filtering
This paper introduces a crowd modeling and motion control approach that
employs diffusion adaptation within an adaptive network. In the network, nodes
collaboratively address specific estimation problems while simultaneously
moving as agents governed by certain motion control mechanisms. Our research
delves into the behaviors of agents when they encounter spatial constraints.
Within this framework, agents pursue several objectives, such as target
tracking, coherent motion, and obstacle evasion. Throughout their navigation,
they demonstrate a nature of self-organization and self-adjustment that drives
them to maintain certain social distances with each other, and adaptively
adjust their behaviors in response to the environmental changes. Our findings
suggest a promising approach to mitigate the spread of viral pandemics and
averting stampedes.Comment: This paper has been submitted to 2024 IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP
Mapping scalp to intracranial eeg using generative adversarial networks for automatically detecting interictal epileptiform discharges
Both scalp and intracranial electroencephalograms (EEGs) are of great importance for diagnosing brain disorders. However, the scalp EEG (sEEG) is attenuated by the skull and contaminated with artifacts. At the same time, intracranial EEG (iEEG) is almost free of artifacts and can capture all brain activities without any attenuation due to being close to the brain sources. In this study, the aim is to enhance the performance of sEEG by mapping the sEEG to the iEEG. To do so, we here develop a deep neural network using a generative adversarial network to estimate the sEEG from the iEEG. The proposed method is applied to sEEG and iEEG recorded simultaneously from epileptics to detect interictal epileptiform discharges (IEDs). The proposed method detects IEDs with 76% accuracy outperforming the state-of-the-art methods. Furthermore, it is at least twelve times less complex than the compared methods
An Adaptive Source-Channel Coding with Feedback for Progressive Transmission of Medical Images
A novel adaptive source-channel coding with feedback for
progressive transmission of medical images is proposed here. In
the source coding part, the transmission starts from the region of
interest (RoI). The parity length in the channel code varies with
respect to both the proximity of the image subblock to the RoI and
the channel noise, which is iteratively estimated in the receiver.
The overall transmitted data can be controlled by the user
(clinician). In the case of medical data transmission, it is vital
to keep the distortion level under control as in most of the cases
certain clinically important regions have to be transmitted
without any visible error. The proposed system significantly
reduces the transmission time and error. Moreover, the system is
very user friendly since the selection of the RoI, its size,
overall code rate, and a number of test features such as noise
level can be set by the users in both ends. A MATLAB-based TCP/IP
connection has been established to demonstrate the proposed
interactive and adaptive progressive transmission system. The
proposed system is simulated for both binary symmetric channel
(BSC) and Rayleigh channel. The experimental results verify the
effectiveness of the design
Underdetermined Blind Identification for -Sparse Component Analysis using RANSAC-based Orthogonal Subspace Search
Sparse component analysis is very popular in solving underdetermined blind
source separation (UBSS) problem. Here, we propose a new underdetermined blind
identification (UBI) approach for estimation of the mixing matrix in UBSS.
Previous approaches either rely on single dominant component or consider active sources at each time instant, where is the number of
mixtures, but impose constraint on the level of noise replacing inactive
sources. Here, we propose an effective, computationally less complex, and more
robust to noise UBI approach to tackle such restrictions when based
on a two-step scenario: (1) estimating the orthogonal complement subspaces of
the overall space and (2) identifying the mixing vectors. For this purpose, an
integrated algorithm is presented to solve both steps based on Gram-Schmidt
process and random sample consensus method. Experimental results using
simulated data show more effectiveness of the proposed method compared with the
existing algorithms
Vision-based techniques for gait recognition
Global security concerns have raised a proliferation of video surveillance
devices. Intelligent surveillance systems seek to discover possible threats
automatically and raise alerts. Being able to identify the surveyed object can
help determine its threat level. The current generation of devices provide
digital video data to be analysed for time varying features to assist in the
identification process. Commonly, people queue up to access a facility and
approach a video camera in full frontal view. In this environment, a variety of
biometrics are available - for example, gait which includes temporal features
like stride period. Gait can be measured unobtrusively at a distance. The video
data will also include face features, which are short-range biometrics. In this
way, one can combine biometrics naturally using one set of data. In this paper
we survey current techniques of gait recognition and modelling with the
environment in which the research was conducted. We also discuss in detail the
issues arising from deriving gait data, such as perspective and occlusion
effects, together with the associated computer vision challenges of reliable
tracking of human movement. Then, after highlighting these issues and
challenges related to gait processing, we proceed to discuss the frameworks
combining gait with other biometrics. We then provide motivations for a novel
paradigm in biometrics-based human recognition, i.e. the use of the
fronto-normal view of gait as a far-range biometrics combined with biometrics
operating at a near distance
Cooperative particle filtering for tracking ERP subcomponents from multichannel EEG
In this study, we propose a novel method to investigate P300 variability over different trials. The method incorporates spatial correlation between EEG channels to form a cooperative coupled particle filtering method that tracks the P300 subcomponents, P3a and P3b, over trials. Using state space systems, the amplitude, latency, and width of each subcomponent are modeled as the main underlying parameters. With four electrodes, two coupled Rao-Blackwellised particle filter pairs are used to recursively estimate the system state over trials. A number of physiological constraints are also imposed to avoid generating invalid particles in the estimation process. Motivated by the bilateral symmetry of ERPs over the brain, the channels further share their estimates with their neighbors and combine the received information to obtain a more accurate and robust solution. The proposed algorithm is capable of estimating the P300 subcomponents in single trials and outperforms its non-cooperative counterpart
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