Artifact Removal from EEG Signals

GuruvaReddy, A.; Narava, Srilatha

doi:10.5120/13543-1175

Cited by 26 publications

(16 citation statements)

References 5 publications

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“…Nevertheless, the low spatial density of the signals registered with Emotiv EPOC+ (only 14 channels) does not allow to apply this methodology. Instead, an adaptive filter should be used to reduce noise in EEG signals (Reddy and Narava, 2013). …”

Section: Methodsmentioning

confidence: 99%

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

Martínez-Rodrigo

Fernández-Sotos²,

Postigo

et al. 2017

Front. Neuroinform.

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This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain–computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form.

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Section: Methodsmentioning

confidence: 99%

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

Martínez-Rodrigo

Fernández-Sotos²,

Postigo

et al. 2017

Front. Neuroinform.

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“…Noise [45] is added during the acquisition of EEG signals that reduces the signal to noise ratio of EEG signals resulting in poor classification between interictal and preictal states [46]. It has been observed that different types of noise affects EEG signals including power line noise [47] of 50 to 60 Hz., basline noise [48] due to interference of multiple electrodes and noise added due to electrical activity of human activities including eye movement and pulse of heart. Therefore, it is very desired to remove noise as preprocessing step from EEG signals in order to increase Signal to Noise ratio for improved classification results.…”

Section: A Pre-processingmentioning

confidence: 99%

Epileptic Seizures Prediction Using Deep Learning Techniques

2020

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Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.

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“…Linear filtering is commonly used for removing artifacts located in certain signals that do not have any overlapped frequency bands with the frequency of the brain signals. Lowpass filtering and high-pass filtering are the most noticeable examples for linear filtering algorithms [23]. High-pass filter is commonly used to remove the EOG artifacts as the EOG artifacts consist of low frequency components.…”

Section: Linear Filteringmentioning

confidence: 99%

“…H(z) is a linear filter which produces an output y(n). To compute the error e(n) the output value y(n) is subtracted from the primary signal d(n) [2,23]. Figure 3 illustrates the linear-adaptive basic structure.…”

Section: Linear Adaptive Filtermentioning

confidence: 99%

Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions

Elsayed¹,

Zaghloul²,

Bayoumi³

2017

IJCA

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Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded signal during the BCI preprocessing stage of to achieve preferable results at the learning stage. This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.

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Artifact Removal from EEG Signals

Cited by 26 publications

References 5 publications

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

Epileptic Seizures Prediction Using Deep Learning Techniques

Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions

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