1,074 research outputs found
Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission
This study investigated the application and enhancement of Non-Invasive Brain-Computer Interfaces (NI-BCIs), focused on enhancing the efficiency and effectiveness of this technology for individuals with severe physical limitations. The core research goal was to improve current limitations associated with wires, noise, and invasive procedures often associated with BCI technology. The key discussed solution involves developing an optimized Encoder/Decoder (E/D) pair using machine learning techniques, particularly those borrowed from Generative Adversarial Networks (GAN) and other Deep Neural Networks, to minimize data transmission and ensure robustness against data degradation. The study highlighted the crucial role of machine learning in self-adjusting and isolating essential data for accurate and efficient classification. The research design involved training this E/D pair to unlock applications of NI EEG BCIs, such as speech synthesis and seamless control of mobile devices. This research successfully trained the E/D pair with a compression ratio of 500 to 75 data points. With parallel processing, this paper successfully processed and transmitted 36 channels of EEG data without data loss at 97% accuracy in 0.0752s. By successfully developing a robust E/D pair, the study aims to revolutionize BCI technology, paving the way for more intuitive interfaces and significantly improving the quality of life for locked-in individuals. This research thus contributes to advancements in NI-BCIs, harnessing machine learning to address current limitations and unlock new possibilities for this critical technology
EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation
The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was
used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed.
However, there is still a need to create a more reliable BCI that could be used to control
several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding
different movements from the same limb, high accuracy and reliability are some of the main
difficulties when using conventional EEG-based BCIs and the challenges we tackled in this
thesis.
In this PhD thesis, we investigated that the classification of several functional hand reaching
movements from the same limb using EEG is possible with acceptable accuracy. Moreover,
we investigated how the recalibration could affect the classification results. For this reason,
we tested the recalibration in each multi-class decoding for within session, recalibrated
between-sessions, and between sessions.
It was shown the great influence of recalibrating the generated classifier with data from the
current session to improve stability and reliability of the decoding. Moreover, we used a
multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the
decoding accuracy based on features and compared it to our previous study using CSP.
Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges
as a way to influence brain plasticity or controlling external devices. However, neural
oscillations have shown to synchronize activity according to motor and cognitive functions.
For this reason, the existence of cross-frequency interactions produces oscillations with
different frequencies in neural networks. In this PhD, we investigated for the first time the
existence of cross-frequency coupling during rest and movement using ECoG in chronic
stroke patients. We found that there is an exaggerated phase-amplitude coupling between
the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also
reported in another neurological disorder affecting motor function (Parkinson and dystonia)
but, to date, it has not been investigated in stroke patients. This finding might change the
future design of assistive or therapeuthic BCI systems for motor restoration in stroke
patients
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available
Recent Applications in Graph Theory
Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks
Brain-Computer Interface
Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems
Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces
This paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications
Development of Brain Machine Interface Systems and its Applications to Prosthetic Hand Control
東京電機大学201
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors
Classifying brain signals collected by wearable Internet of Things (IoT)
sensors, especially brain-computer interfaces (BCIs), is one of the
fastest-growing areas of research. However, research has mostly ignored the
secure storage and privacy protection issues of collected personal
neurophysiological data. Therefore, in this article, we try to bridge this gap
and propose a secure privacy-preserving protocol for implementing BCI
applications. We first transformed brain signals into images and used
generative adversarial network to generate synthetic signals to protect data
privacy. Subsequently, we applied the paradigm of transfer learning for signal
classification. The proposed method was evaluated by a case study and results
indicate that real electroencephalogram data augmented with artificially
generated samples provide superior classification performance. In addition, we
proposed a blockchain-based scheme and developed a prototype on Ethereum, which
aims to make storing, querying and sharing personal neurophysiological data and
analysis reports secure and privacy-aware. The rights of three main transaction
bodies - construction workers, BCI service providers and project managers - are
described and the advantages of the proposed system are discussed. We believe
this paper provides a well-rounded solution to safeguard private data against
cyber-attacks, level the playing field for BCI application developers, and to
the end improve professional well-being in the industry
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