Increasing the learning Capacity of BCI Systems via CNN-HMM models

Saidutta, Yashas Malur; Zou, Jun; Fekri, Faramarz

doi:10.1109/embc.2018.8512714

Cited by 6 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, some papers [ 10 , 11 , 57 ] obtained good performance, 98.81%, 95.33% and 92%, respectively, even though they did not use any preprocessing step. However, Jeong and colleagues and Saidutta and colleagues [ 26 , 58 ], using automated and advanced preprocessing, reached a performance of 87% and 81%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…CNN is widely used, in the reviewed literature, to discover the latent spatial information in applications such as the analysis of motor imagery data [ 40 ], robotics [ 65 , 83 ]. increasing the learning capacity of BCI systems [ 58 ], detecting depression with EEG signals and to evaluate a novel deep learning method for classifying binary motor imagery data [ 41 ]. Some studies propose new network structures that mix CNN with representation algorithms for feature extraction and classification.…”

Section: Table A1mentioning

confidence: 99%

“…On the other hand, Nonlinear Bayesian classifiers are classifiers modelling the probability distributions of each class and use the Bayes rule to select the class to assign to the current feature vector. The Hidden Markov Model (HMMs) [ 58 ] can be represented as the simplest dynamic Bayesian network. Secondly, Neural Networks (NN) [ 43 ] can be used to approximate any non-linear decision boundary.…”

Section: Table A1mentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends. Results: Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency. Significance: To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In recent years, deep-learning algorithms have achieved rapid progress in many fields such as image classification, speech recognition, and recommendation system. Deep-learning algorithms have also been applied in the field of SSVEP-BCI system classification algorithm such as convolutional neural network (CNN) [76], recycle neural network [77], and long short-term memory (LSTM) [78]. However, the deep-learning algorithm has not shown significant advantages from a comprehensive view of these articles on SSVEP-BCI, which may be due to the difficulty for scholars to obtain a large number of EEG data and the high SNR of EEG data.…”

Section: Deep-learning Algorithmmentioning

confidence: 99%

Review of brain–computer interface based on steady‐state visual evoked potential

Liu

Zhang

Liu

et al. 2022

Brain Science Advances

View full text Add to dashboard Cite

The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady‐state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal‐to‐noise ratio and short training‐time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

show abstract

“…All these experiments using RNNs on top of CNNs for biomedical signal analysis were successful to produce extremely high levels of abstraction and rich temporal representation that can perceive long range contexts without human intervention in addition to being easier to optimize computationally. CNNs have been also utilized in association with fully connected networks to increase the capacity of HMMs in connectionist hybrid DNN-HMM models due to the ability of CNNs to process high-dimensional multi-step inputs [213]. Such hybrid systems provided state of the art performance especially in the field of handwriting recognition [214,215].…”

Section: High Capacity Models Embedding Feature Extractionmentioning

confidence: 99%

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

2021

View full text Add to dashboard Cite

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.

show abstract

Increasing the learning Capacity of BCI Systems via CNN-HMM models

Cited by 6 publications

References 9 publications

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

Review of brain–computer interface based on steady‐state visual evoked potential

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

Contact Info

Product

Resources

About