An automated detection and correction method of EOG artifacts in EEG-based BCI

Wu, Jin; Zhang, Jiacai; Yao, Li

doi:10.1109/iccme.2009.4906624

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…The pre-processing of intracranial EEG signals is relatively simple to avoid removing valuable information. Baseline drift was removed by subtracting the mean value of the iEEG signal from each data point ( Wu et al, 2009 ). Then a simple fourth-order Butterworth bandpass filter with a range of 0.5∼70 Hz was used to filter the iEEG signal.…”

Section: Methodsmentioning

confidence: 99%

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

Zhong

Wan

et al. 2023

Front. Neurosci.

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ObjectiveEpilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients’ lives.MethodsFrom the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset.ResultsThis model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features.ConclusionCompared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.

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Section: Methodsmentioning

confidence: 99%

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

Zhong

Wan

et al. 2023

Front. Neurosci.

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“…Some others are based in a supervised learning method using statistical [4] or autoregressive (AR) [5] features. Other methods, like [6], use a combination of feature extraction and data driven thresholds.…”

Section: Introductionmentioning

confidence: 99%

“…The first one aims in the removal of the EOG captured signal, which is captured with a separate sensor, with the use of linear combination and regression techniques [6]. The second category of removal methods aims in the clarification of the components-sources that compose the recorded data and afterwards the identification of the artifact related ones.…”

Section: Introductionmentioning

confidence: 99%

Eye Blink Artifact Removal in EEG Using Tensor Decomposition

Triantafyllopoulos

Megalooikonomou

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 2: MHDW WorkshopInternational audienceEEG data are usually contaminated with signals related to subject’s activities, the so called artifacts, which degrade the information contained in recordings. The removal of this additional information is essential to the improvement of EEG signals’ interpretation. The proposed method is based on the analysis, using Tucker decomposition, of a tensor constructed using continuous wavelet transform. Our contribution is an automatic method which processes simultaneously spatial, temporal and frequency information contained in EEG recordings in order to remove eye blink related information. The proposed method is compared with a matrix based removal method and shows promising results regarding reconstruction error and retaining the texture of the artifact free signal

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