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Ribeiro MHDM, da Silva RG, Larcher JHK, Mendes A, Mariani VC, Coelho LDS. Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution. SENSORS (BASEL, SWITZERLAND) 2023; 23:7049. [PMID: 37631586 PMCID: PMC10459492 DOI: 10.3390/s23167049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023]
Abstract
Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK's layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.
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Affiliation(s)
- Matheus Henrique Dal Molin Ribeiro
- Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Paraná (PUCPR), R. Imaculada Conceição 1155, Curitiba 80215-901, PR, Brazil;
- Department of Mathematics, Federal University of Technology—Paraná (UTFPR), Via do Conhecimento, KM 01—Fraron, Pato Branco 85503-390, PR, Brazil
| | - Ramon Gomes da Silva
- Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Paraná (PUCPR), R. Imaculada Conceição 1155, Curitiba 80215-901, PR, Brazil;
| | - José Henrique Kleinubing Larcher
- Mechanical Engineering Graduate Program (PPGEM), Pontifical Catholic University of Paraná (PUCPR), R. Imaculada Conceição 1155, Curitiba 80215-901, PR, Brazil; (J.H.K.L.); (V.C.M.)
| | - Andre Mendes
- Department of Economics, Massachusetts Institute of Technology, 292 Main St, Cambridge, MA 02142, USA;
| | - Viviana Cocco Mariani
- Mechanical Engineering Graduate Program (PPGEM), Pontifical Catholic University of Paraná (PUCPR), R. Imaculada Conceição 1155, Curitiba 80215-901, PR, Brazil; (J.H.K.L.); (V.C.M.)
- Department of Electrical Engineering, Federal University of Paraná (UFPR), R. Evaristo F. Ferreira da Costa 384, Curitiba 81530-000, PR, Brazil
| | - Leandro dos Santos Coelho
- Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Paraná (PUCPR), R. Imaculada Conceição 1155, Curitiba 80215-901, PR, Brazil;
- Department of Electrical Engineering, Federal University of Paraná (UFPR), R. Evaristo F. Ferreira da Costa 384, Curitiba 81530-000, PR, Brazil
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He F, Yang Y. Nonlinear System Identification of Neural Systems from Neurophysiological Signals. Neuroscience 2021; 458:213-228. [PMID: 33309967 PMCID: PMC7925423 DOI: 10.1016/j.neuroscience.2020.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 12/20/2022]
Abstract
The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.
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Affiliation(s)
- Fei He
- Centre for Data Science, Coventry University, Coventry CV1 2JH, UK
| | - Yuan Yang
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Tulsa, OK 74135, USA; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Laureate Institute for Brain Research, Tulsa, OK 74136, USA
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Guo Y, Wang L, Li Y, Guo L, Meng F. The Detection of Freezing of Gait in Parkinson's Disease Using Asymmetric Basis Function TV-ARMA Time-Frequency Spectral Estimation Method. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2077-2086. [PMID: 31478865 DOI: 10.1109/tnsre.2019.2938301] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Freezing of gait (FOG) is an episodic gait disturbance affecting locomotion in Parkinson's disease. As a biomarker to detect FOG, the Freeze index (FI), which is defined as the ratio of the areas under power spectra in 'freeze' band and in 'locomotion' band, can negatively be affected by poor time and frequency resolution of time-frequency spectrum estimate when short-time Fourier transform (STFT) or Wavelet transform (WT) is used. In this study, a novel high-resolution parametric time-frequency spectral estimation method is proposed to improve the accuracy of FI. A time-varying autoregressive moving average model (TV-ARMA) is first identified where the time-varying parameters are estimated using an asymmetric basis function expansion method. The TV-ARMA model is then transformed into frequency domain to estimate the time-frequency spectrum and calculate the FI. Results evaluated on the Daphnet Freezing of Gait Dataset show that the new method improves the time and frequency resolutions of the time-frequency spectrum and the associate FI has better performance in the detection of FOG than its counterparts based on STFT and WT methods do. Moreover, FOGs can be predicted in advance of its occurrence in most cases using the new method.
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