74 research outputs found
Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off
Electroencephalography (EEG) datasets are often small and high dimensional, owing to
cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of
dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in
EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally
intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this
paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to
Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and
Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show
that the optimization procedure improves accuracy in all models, and that CNN models with
only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief
Network. FFNN and RNN were not able to reach the same quality, although the cost was
significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or
even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing
factor since deep learning approaches struggle with limited training examples.Spanish Ministerio de Ciencia, Innovacion y Universidades
PGC2018-098813-B-C31
PGC2018-098813-B-C32
PSI201565848-
Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples
A distributed and energyβefficient KNN for EEG classification with dynamic moneyβsaving policy in heterogeneous clusters
Universidad de Granada/CBUASpanish Ministry of Science, Innovation, and Universities under Grants PGC2018-098813-B-C31,PID2022-137461NB-C32ERDF fund. Funding for open access charge: University of Granada/
CBU
ΠΠ΅ΡΠΎΠ΄Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΠΠ-ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠ² Π²ΠΎΠΎΠ±ΡΠ°ΠΆΠ°Π΅ΠΌΡΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉ
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π½Π΅ΠΈΠ½Π²Π°Π·ΠΈΠ²Π½ΡΡ
ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠΎΠ² ΠΌΠΎΠ·Π³βΠΊΠΎΠΌΠΏΡΡΡΠ΅Ρ ΠΈ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Π΄Π»Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠ². ΠΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±Π·ΠΎΡ ΡΠ°Π±ΠΎΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΡ
Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΈΠΌΠ°Π½ΠΎΠ²Ρ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡ, ΠΌΠ΅ΡΠΎΠ΄Ρ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ Π²Π°ΡΠΈΠ°Π½ΡΡ ΠΏΡΠ΅Π΄ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ², Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°. Π‘ΡΠ΅Π΄ΠΈ ΠΏΡΠΎΡΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΏΡΠ΅Π΄ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° ΡΠ°ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ Π² ΠΎΡΠ»Π°ΠΉΠ½, ΡΠ°ΠΊ ΠΈ Π² ΠΎΠ½Π»Π°ΠΉΠ½ ΡΠ΅ΠΆΠΈΠΌΠ°Ρ
. Π‘ΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΠΌ ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΡ
Π»Π΅Ρ ΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°, Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΈΡΠΊΡΠΈΠΌΠΈΠ½Π°Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² ΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ Ρ ΠΎΠ±ΡΠ°ΡΠ½ΡΠΌ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΡΠΈΠ±ΠΊΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΡ 91% ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΈ Π΄Π²ΡΡ
ΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Ρ ΠΎΠ±ΡΠ°ΡΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΡΡ Π² Π²ΠΈΠ΄Π΅ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΊΠ·ΠΎΡΠΊΠ΅Π»Π΅ΡΠΎΠΌ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΎΠ½Π»Π°ΠΉΠ½ ΠΎΡΠ΅Π½Ρ ΠΌΠ°Π»ΠΎ, ΠΈ Π½Π° Π΄Π°Π½Π½ΡΠΉ ΠΌΠΎΠΌΠ΅Π½Ρ Π½Π°ΠΈΠ»ΡΡΡΠ°Ρ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΏΡΠΈ Π΄Π²ΡΡ
ΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 69,3%. ΠΡΠΈ ΡΡΠΎΠΌ Π² ΠΎΡΠ»Π°ΠΉΠ½ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΡΠ΅Π΄Π½ΠΈΠΉ ΠΏΡΠΎΡΠ΅Π½Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π² ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΡ
ΡΡΠ°ΡΡΡΡ
Π΄Π»Ρ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° β 77,5Β±5,8%, ΡΠ΅ΡΠ΅ΠΉ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ β 81,7Β±4,7%, ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ β 90,2Β±6,6%. ΠΠ° ΡΡΠ΅Ρ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄Ρ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π° ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ Π±ΠΎΠ»ΡΡΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΈ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΊ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ»Π΅Π·Π½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΡΠΈΠ³Π½Π°Π»Π° ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΡΠΌ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π²Π°ΠΆΠ½Π° Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ ΡΠΎΡΠ½ΠΎΡΡΡ, Π½ΠΎ ΠΈ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½Π°Ρ Π²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ Π·Π°Π΄Π΅ΡΠΆΠΊΠ°. ΠΠ΄Π΅ΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ Π·Π° ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°ΠΌΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° ΠΈ ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ Ρ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π΅ΡΠΆΠΊΠΎΠΉ ΠΌΠ΅Π½Π΅Π΅ 500 ΠΌΡ
ΠΠ΅ΡΠΎΠ΄Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΠΠ-ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠ² Π²ΠΎΠΎΠ±ΡΠ°ΠΆΠ°Π΅ΠΌΡΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉ
The review focuses on the most promising methods for classifying EEG signals for non-invasive BCIs and theoretical approaches for the successful classification of EEG patterns. The paper provides an overview of articles using Riemannian geometry, deep learning methods and various options for preprocessing and "clustering" EEG signals, for example, common-spatial pattern (CSP). Among other approaches, pre-processing of EEG signals using CSP is often used, both offline and online. The combination of CSP, linear discriminant analysis, support vector machine and neural network (BPNN) made it possible to achieve 91% accuracy for binary classification with exoskeleton control as a feedback. There is very little work on the use of Riemannian geometry online and the best accuracy achieved so far for a binary classification problem is 69.3% in the work. At the same time, in offline testing, the average percentage of correct classification in the considered articles for approaches with CSP β 77.5 Β± 5.8%, deep learning networks β 81.7 Β± 4.7%, Riemannian geometry β 90.2 Β± 6.6%. Due to nonlinear transformations, Riemannian geometry-based approaches and complex deep neural networks provide higher accuracy and better extract of useful information from raw EEG recordings rather than linear CSP transformation. However, in real-time setup, not only accuracy is important, but also a minimum time delay. Therefore, approaches using the CSP transformation and Riemannian geometry with a time delay of less than 500 ms may be in the future advantage.Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π½Π΅ΠΈΠ½Π²Π°Π·ΠΈΠ²Π½ΡΡ
ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠΎΠ² ΠΌΠΎΠ·Π³βΠΊΠΎΠΌΠΏΡΡΡΠ΅Ρ ΠΈ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Π΄Π»Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠ². ΠΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±Π·ΠΎΡ ΡΠ°Π±ΠΎΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΡ
Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΈΠΌΠ°Π½ΠΎΠ²Ρ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡ, ΠΌΠ΅ΡΠΎΠ΄Ρ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ Π²Π°ΡΠΈΠ°Π½ΡΡ ΠΏΡΠ΅Π΄ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ², Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°. Π‘ΡΠ΅Π΄ΠΈ ΠΏΡΠΎΡΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΏΡΠ΅Π΄ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° ΡΠ°ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ Π² ΠΎΡΠ»Π°ΠΉΠ½, ΡΠ°ΠΊ ΠΈ Π² ΠΎΠ½Π»Π°ΠΉΠ½ ΡΠ΅ΠΆΠΈΠΌΠ°Ρ
. Π‘ΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΠΌ ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΡ
Π»Π΅Ρ ΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°, Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΈΡΠΊΡΠΈΠΌΠΈΠ½Π°Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² ΠΈ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ Ρ ΠΎΠ±ΡΠ°ΡΠ½ΡΠΌ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΡΠΈΠ±ΠΊΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π΄ΠΎΡΡΠΈΠ³Π½ΡΡΡ 91% ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΈ Π΄Π²ΡΡ
ΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Ρ ΠΎΠ±ΡΠ°ΡΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΡΡ Π² Π²ΠΈΠ΄Π΅ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΊΠ·ΠΎΡΠΊΠ΅Π»Π΅ΡΠΎΠΌ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΎΠ½Π»Π°ΠΉΠ½ ΠΎΡΠ΅Π½Ρ ΠΌΠ°Π»ΠΎ, ΠΈ Π½Π° Π΄Π°Π½Π½ΡΠΉ ΠΌΠΎΠΌΠ΅Π½Ρ Π½Π°ΠΈΠ»ΡΡΡΠ°Ρ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΏΡΠΈ Π΄Π²ΡΡ
ΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 69,3%. ΠΡΠΈ ΡΡΠΎΠΌ Π² ΠΎΡΠ»Π°ΠΉΠ½ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΡΠ΅Π΄Π½ΠΈΠΉ ΠΏΡΠΎΡΠ΅Π½Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π² ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΡ
ΡΡΠ°ΡΡΡΡ
Π΄Π»Ρ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° β 77,5Β±5,8%, ΡΠ΅ΡΠ΅ΠΉ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ β 81,7Β±4,7%, ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ β 90,2Β±6,6%. ΠΠ° ΡΡΠ΅Ρ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄Ρ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π° ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ Π±ΠΎΠ»ΡΡΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΈ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΊ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ»Π΅Π·Π½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΡΠΈΠ³Π½Π°Π»Π° ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΡΠΌ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π²Π°ΠΆΠ½Π° Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ ΡΠΎΡΠ½ΠΎΡΡΡ, Π½ΠΎ ΠΈ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½Π°Ρ Π²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ Π·Π°Π΄Π΅ΡΠΆΠΊΠ°. ΠΠ΄Π΅ΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²ΠΎ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ Π·Π° ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°ΠΌΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° ΠΈ ΡΠΈΠΌΠ°Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΠΈ Ρ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π΅ΡΠΆΠΊΠΎΠΉ ΠΌΠ΅Π½Π΅Π΅ 500 ΠΌΡ
EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs
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