Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients

Miladinović, Aleksandar; Ajčević, Miloš; Busan, Pierpaolo; Jarmolowska, Joanna; Silveri, Giulia; Mezzarobba, Susanna; Battaglini, Piero Paolo; Accardo, Agostino

doi:10.23919/eusipco47968.2020.9287391

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Indeed, EEG changes over time can introduce nonstationarities and diminish BCI accuracy [24]. The intersession transfer learning techniques improve the performance in PDs [25], but its further advance requires a better understanding of the PD pathology related EEG.…”

Section: Introductionmentioning

confidence: 99%

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Miladinović

Ajčević

Busan

et al. 2021

Procedia Computer Science

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Section: Introductionmentioning

confidence: 99%

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Miladinović

Ajčević

Busan

et al. 2021

Procedia Computer Science

Self Cite

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“…This problem severely limits the real-world application of BCI systems. To address the problem, researchers have presented four main types of approaches that are based on regularization [ 11 , 12 ], user-to-user transfer [ 13 – 16 ], semi-supervised learning [ 17 – 19 ] and a prior physiological information [ 20 , 21 ]. Recently, a novel approach was proposed for reducing calibration time that is to generate artificial EEG data from a few actual training data available and use them to augment the training set.…”

Section: Introductionmentioning

confidence: 99%

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition

Xiong

Wei

2022

PLoS ONE

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One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.

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Parkinson’s Disease Detection by Using Machine Learning Method based on Local Classification on Class Boundary

Du,

Shen,

Wen

et al. 2024

Discov Appl Sci

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Parkinson’s disease (PD) detection has long been an important task in medical intelligence. Recognition methods based on speech signals show great potential in Parkinson’s disease diagnosis. In this paper, based on an efficient machine learning method for Parkinson’s disease detection, we take the use of test data incorporates an efficient Secure Two-Party Computing (S2PC) protocol to protect the privacy of patients. We present two key components, the secure use of data and a local classification methodology, including the description of class boundaries. We conducted experiments on two datasets to validate our proposed method, and the results show well data security protection ability compared to some more sophisticated methods. And the performance of Local Classification on Class Boundary(LCCB) and Hyperplane K-Nearest Neighbor(HKNN) is significantly better than that of both Support Vector Machines(SVM) and Random Forest(RF). When the number of selected features is from 400 to 500, HKNN and LCCB are roughly equal where the accuracy of HKNN is 95.2%, and LCCB has the rate of 94.7%. Then we use Multi-Cluster Feature Selection(MCFS) to analyze and select the important features from D2 dataset. It shows that even if only two features are selected, the boundaries of the two categories are also clear and easy to distinguish.

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Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients

Cited by 5 publications

References 13 publications

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition

Parkinson’s Disease Detection by Using Machine Learning Method based on Local Classification on Class Boundary

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