404 research outputs found
Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications
The activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, the two most essential steps are feature extraction and classification method. However, in this paper, the MI classification is improved by the performance of Deep Learning (DL) concept. In this proposed system two-moment imagination of right hand and right foot from the BCI competition three datasets IVA has been taken and classification methods utilizing Conventional neural network (CNN) and Generative Adversarial Network (GAN) are developed. The training time is reduced and non-stationary problem is managed by applying Empirical mode decomposition (EMD) and mixing their intrinsic mode functions (IMFs) in feature extraction technique. The experimental result indicates the proposed GAN classification technique achieves better classification accuracy in terms of 95.29% than the CNN of 89.38%. The proposed GAN method achieves an average positive rate of 62% and average false positive rate of 3.4% on BCI competition three datasets IVA whose EEG facts were resulting from the similar C3, C4, and Cz channels of the motor cortex
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network
for Motor Imagery decoding in BCI. Our main motivation is to propose a very
simple baseline to compare to, using only very standard ingredients from the
literature. We evaluate its performance on four EEG Motor Imagery datasets,
including simulated online setups, and compare it to recent Deep Learning and
Machine Learning approaches. EEG-SimpleConv is at least as good or far more
efficient than other approaches, showing strong knowledge-transfer capabilities
across subjects, at the cost of a low inference time. We advocate that using
off-the-shelf ingredients rather than coming with ad-hoc solutions can
significantly help the adoption of Deep Learning approaches for BCI. We make
the code of the models and the experiments accessible
Determining States of Movement in Humans Using Minimally Processed EEG Signals and Various Classification Methods
Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without taking biofeedback such as brain state into account. Additionally, the administration of tDCS by a technician during motor tasks is a tiresome process. Machine learning and deep learning algorithms are often used to perform classification tasks on high-dimensional data, and have been successfully used to classify movement states based on EEG features. In this thesis, a program capable of performing live classification of motor state using machine learning and EEG as biofeedback is proposed. This program would allow for the development of a device that optimally administers tDCS dosage during motor tasks. This is achieved by surveying the literature for motor classification techniques based on EEG signals, recreating the methods in the surveyed literature, measuring their accuracy, and creating an application to perform online capturing and analysis of EEG recordings using the classifier with the highest accuracy to demonstrate the feasibility of real-time classification. The highest accuracy of motor classification is achieved by training a random forest on binned spectral decomposition from a normalized signal. While live classification was successfully performed, accuracy was limited by external changes to the recording environment, skewing the input to the trained model
Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification
Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor
images recognized from the electroencephalogram (EEG) to control commands. EEG
patterns of different imagination tasks, e.g. hand and foot movements, are
effectively classified with machine learning techniques using band power
features. Recently, also Convolutional Neural Networks (CNNs) that learn both
effective features and classifiers simultaneously from raw EEG data have been
applied. However, CNNs have two major drawbacks: (i) they have a very large
number of parameters, which thus requires a very large number of training
examples; and (ii) they are not designed to explicitly learn features in the
frequency domain. To overcome these limitations, in this work we introduce
Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass
and depthwise convolutional filters. Experimental results obtained on the
publicly available BCI Competition IV Dataset 2a show that our approach
outperforms reference methods in terms of classification accuracy
Study of soft materials, flexible electronics, and machine learning for fully portable and wireless brain-machine interfaces
Over 300,000 individuals in the United States are afflicted with some form of limited motor function from brainstem or spinal-cord related injury resulting in quadriplegia or some form of locked-in syndrome. Conventional brain-machine interfaces used to allow for communication or movement require heavy, rigid components, uncomfortable headgear, excessive numbers of electrodes, and bulky electronics with long wires that result in greater data artifacts and generally inadequate performance. Wireless, wearable electroencephalograms, along with dry non-invasive electrodes can be utilized to allow recording of brain activity on a mobile subject to allow for unrestricted movement. Additionally, multilayer microfabricated flexible circuits, when combined with a soft materials platform allows for imperceptible wearable data acquisition electronics for long term recording. This dissertation aims to introduce new electronics and training paradigms for brain-machine interfaces to provide remedies in the form of communication and movement for these individuals. Here, training is optimized by generating a virtual environment from which a subject can achieve immersion using a VR headset in order to train and familiarize with the system. Advances in hardware and implementation of convolutional neural networks allow for rapid classification and low-latency target control. Integration of materials, mechanics, circuit and electrode design results in an optimized brain-machine interface allowing for rehabilitation and overall improved quality of life.Ph.D
Deep comparisons of Neural Networks from the EEGNet family
Most of the Brain-Computer Interface (BCI) publications, which propose
artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG)
signal classification, are presented using one of the BCI Competition datasets.
However, these databases contain MI EEG data from less than or equal to 10
subjects . In addition, these algorithms usually include only bandpass
filtering to reduce noise and increase signal quality. In this article, we
compared 5 well-known neural networks (Shallow ConvNet, Deep ConvNet, EEGNet,
EEGNet Fusion, MI-EEGNet) using open-access databases with many subjects next
to the BCI Competition 4 2a dataset to acquire statistically significant
results. We removed artifacts from the EEG using the FASTER algorithm as a
signal processing step. Moreover, we investigated whether transfer learning can
further improve the classification results on artifact filtered data. We aimed
to rank the neural networks; therefore, next to the classification accuracy, we
introduced two additional metrics: the accuracy improvement from chance level
and the effect of transfer learning. The former can be used with different
class-numbered databases, while the latter can highlight neural networks with
sufficient generalization abilities. Our metrics showed that the researchers
should not avoid Shallow ConvNet and Deep ConvNet because they can perform
better than the later published ones from the EEGNet family
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