Convolutional neural network and riemannian geometry hybrid approach for motor imagery classification

Gao, Chang; Li, Wenchao; Xian, Yuezhong

doi:10.1016/j.neucom.2022.08.024

Cited by 26 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Riemannian neural networks, which rise the accuracy by determining the geodesic, one prefers to apply Apollo and AdaHessian, because with gradient directions they analyze the curvature of loss function. But for cases of Riemannian convolutional neural networks [130] they can not reduce time and power consumption, and applying first-oder optimization algorithm one loses the test accuracy. Therefore, it is necessary to engage algorithms based on information geometry.…”

Section: Application Of Optimization Methods In Modern Neural Networkmentioning

confidence: 99%

Survey of Optimization Algorithms in Modern Neural Networks

Abdulkadirov¹,

Lyakhov²,

Nagornov³

2023

Preprint

View full text Add to dashboard Cite

Creating self-learning algorithms, developing deep neural networks and improving other methods that "learn" for various areas of human activity is the main goal of the theory of machine learning. It helps to replace the human with a machine, aiming to increase the quality of production. The theory of artificial neural networks, which already have replaced the humans in problems of detection of moving objects, recognition of images or sounds, time series prediction, big data analysis and numerical methods remains the most dispersed branch of the theory of machine learning. Certainly, for each area of human activity it is necessary to select appropriate neural network architectures, methods of data processing and some novel tools from applied mathematics. But the universal problem for all these neural networks with specific data is the achieving the highest accuracy in short time. Such problem can be resolved by increasing sizes of architectures and improving data preprocessing, where the accuracy rises with the training time. But there is a possibility to increase the accuracy without time growing, applying optimization methods. In this survey we demonstrate existing optimization algorithms of all types, which can be used in neural networks. There are presented modifications of basic optimization algorithms, such as stochastic gradient descent, adaptive moment estimation, Newton and quasi-Newton optimization methods. But the most recent optimization algorithms are related to information geometry, for Fisher-Rao and Bregman metrics. This approach in optimization extended the theory of classic neural networks to quantum and complex-valued neural networks, due to geometric and probabilistic tools. There are provided applications of all introduced optimization algorithms, what delighted many kinds of neural networks, which can be improved by including any advanced approaches in minimization of the loss function. Afterwards, we demonstrated ways of developing optimization algorithms in further researches, engaging neural networks with progressive architectures. Classical gradient based optimizers can be replaced by fractional order, bilevel and, even, gradient free optimization methods. There is a possibility to add such analogues in graph, spiking, complex-valued, quantum and wavelet neural networks. Besides the usual problems of image recognition, time series prediction, object detection, there are many are other tasks for modern theory of machine learning, such as solving problem of quantum computations, partial differential and integro-differential equations, stochastic processes and Brownian motion, making decisions and computer algebra.

show abstract

Section: Application Of Optimization Methods In Modern Neural Networkmentioning

confidence: 99%

Survey of Optimization Algorithms in Modern Neural Networks

Abdulkadirov¹,

Lyakhov²,

Nagornov³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The optimal RF may vary from participant to participant. It may even change for the same participant ( Gao et al, 2022 ). Therefore, we propose the MRF-CNN approach, combining CNNs with multiple RFs, as shown in Figure 4 .…”

Section: Methodsmentioning

confidence: 99%

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network

Park

Lee

2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

IntroductionIn this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study participants.Materials and methodsFirst, two different signal decomposition methods were applied for comparison: noise-assisted multivariate empirical mode decomposition and wavelet packet decomposition. Six statistical features were calculated from the decomposed eight sub-frequency bands EEG. Next, all features obtained from each channel of the trial were vectorized and used as the input vector of classifiers. Lastly, EEG was classified using multireceptive field convolutional neural network and several other classifiers for comparison.ResultsWe achieved an average classification rate of 73.09 and up to 80.41% in a multiclass (six classes) setup (Chance: 16.67%). In comparison with various other classifiers, significant improvements for other classifiers were achieved (p-value < 0.05). From the frequency sub-band analysis, high-frequency band regions and the lowest-frequency band region contain more information about imagined vowel EEG data. The misclassification and classification rate of each vowel imaginary EEG was analyzed through a confusion matrix.DiscussionImagined speech EEG can be classified successfully using the proposed signal decomposition method and a convolutional neural network. The proposed classification method for imagined speech EEG can contribute to developing a practical imagined speech-based brain-computer interfaces system.

show abstract

“…For the publicly available datasets, the maximum number was nine subjects from BCI Competition dataset II a. Moreover, most of the papers in the current literature do not consider the classification of the bilateral upper limbs (both fists) [5,13,23,24,26,29,32,[37][38][39]. We have found that a very limited number of papers in the surveyed literature have used ME in the classification.…”

Section: Introductionmentioning

confidence: 99%

Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets

Shuqfa

Belkacem

Lakas

2023

Sensors

View full text Add to dashboard Cite

The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In the study, we apply several Riemannian geometry decoding algorithms applied on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.

show abstract

Convolutional neural network and riemannian geometry hybrid approach for motor imagery classification

Cited by 26 publications

References 25 publications

Survey of Optimization Algorithms in Modern Neural Networks

Survey of Optimization Algorithms in Modern Neural Networks

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network

Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets

Contact Info

Product

Resources

About