Classification of Mental Tasks From Eeg Signals Using Extreme Learning Machine

Liang, Nanying; Saratchandran, P.; Huang, Guang-Bin; Sundararajan, N.

doi:10.1142/s0129065706000482

Cited by 232 publications

(109 citation statements)

References 13 publications

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…The training of the network in the conventional feed-forwarded ANN is to look for the LS solution in a linear equation Hβ = Y in the ELM. The ELM algorithm can be summarized in 3 steps as follows briefly [5,25] …”

Section: Extreme Learning Machinementioning

confidence: 99%

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Kaya¹

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.

show abstract

Section: Extreme Learning Machinementioning

confidence: 99%

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Kaya¹

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…3.5 ELM based training algorithm Liang et al (2006) has showing the ability of the SLFN network to fix the network connection at one layer with the weights between input neurons and hidden neurons. The same goes to the output neurons where there is fix network connection with weights between hidden neurons and output neurons.…”

Section: Object Recognition 136mentioning

confidence: 99%

Vehicle Recognition System Using Singular Value Decomposition and Extreme Learning Machine

Saad¹,

Osman²,

Isa³

et al. 2011

Object Recognition

View full text Add to dashboard Cite

“…The EEG signals undergo changes in the amplitude as well as frequency, while different mental tasks are performed [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

A Pervasive Approach to EEG‐Based Depression Detection

et al. 2018

View full text Add to dashboard Cite

Nowadays, depression is the world's major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe threeelectrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers' performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.

show abstract

Classification of Mental Tasks From Eeg Signals Using Extreme Learning Machine

Cited by 232 publications

References 13 publications

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Vehicle Recognition System Using Singular Value Decomposition and Extreme Learning Machine

A Pervasive Approach to EEG‐Based Depression Detection

Contact Info

Product

Resources

About