Artifact removal from EEG signals recorded in non-restricted environment

Jamil, Zainab; Jamil, Afshan; Majid, Muhammad

doi:10.1016/j.bbe.2021.03.009

Cited by 17 publications

(5 citation statements)

References 32 publications

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“…Deep learning methods are promising to remove all the artifacts but to be explored more for real time BCI applications. The deep learning methods showed higher SNR and low root mean square error (RMSE) as shown in [148,128]. Even though there are many methods available, there is no single specific solution for removing all types of artifacts.…”

Section: Ica-cca 11%mentioning

confidence: 99%

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

2022

IJATEE

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The artifacts may affect the signal in spectral, temporal and in few cases it affects spatial domain as well which makes it difficult to process. In this case, simple filtering is not sufficient to completely remove the artifacts during pre-processing. Hence, many hybrid methods were developed but still there is no single method to detect and remove all types of artifacts [2].In this context, this paper provides a systematic literature review on types of artifacts, existing methods and comparative analysis on available methods. Further, performance evaluation metrics and available open-source tools for artifact removal are also discussed. The preferred reporting items for

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Section: Ica-cca 11%mentioning

confidence: 99%

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

2022

IJATEE

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“…Como dito anteriormente, os sinais de EEG são sujeitos à muitos ruídos [Jamil et al 2021], os quais podem atrapalhar na identificac ¸ão de um indivíduo pela rede neural, e diminuir a acurácia da mesma. Deste modo, foi avaliado passar cada um dos sinais de um EEG em um filtro passa-banda, na tentativa de melhorar a qualidade dos sinais e dos dados obtidos por meio deles.…”

Section: Pré-processamentounclassified

Rede Neural Convolucional e LSTM para Biometria Baseada em EEG no Modo de Identificação

Freitas

Moreira²,

Luz³

2022

Anais Do XXII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2022)

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Com o avanço da biometria e a necessidade de sistemas de segurança mais robustos, outros tipos de características humanas além das mais utilizadas foram levadas em consideração no desenvolvimento de sistemas biométricos. Uma destas características é o eletroencefalograma (sinais cerebrais). Este trabalho então avalia uma rede neural, cuja arquitetura combina camadas de Redes Neurais Convolucionais e camadas de Long Short-Term Memory (LSTM), em um sistema biométrico no modo de identificação, e utiliza os dados dos 109 indivíduos presentes na base de dados EEG Motor Movement/Imagery Dataset. Ao utilizar um tamanho de janela de 12 seg., um resultado estado-da-arte de 99,7% de acurácia foi atingido, provando a eficiência da metodologia aplicada.

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“…us, the value of the channel is the depth of the convolution. erefore, the time complexity of the convolution algorithm is obviously positively correlated with the area of the convolution layer and the feature vector [26][27][28].…”

Section: Feature Learning Algorithm Based On Cnnanddbn To Extract Mus...mentioning

confidence: 99%

Music Score Recognition Method Based on Deep Learning

Lin

2022

Computational Intelligence and Neuroscience

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In recent years, the recommendation application of artificial intelligence and deep music has gradually become a research hotspot. As a complex machine learning algorithm, deep learning can extract features with value laws through training samples. The rise of deep learning network will promote the development of artificial intelligence and also provide a new idea for music score recognition. In this paper, the improved deep learning algorithm is applied to the research of music score recognition. Based on the traditional neural network, the attention weight value improved convolutional neural network (CNN) and high execution efficiency deep belief network (DBN) are introduced to realize the feature extraction and intelligent recognition of music score. Taking the feature vector set extracted by CNN-DBN as input set, a feature learning algorithm based on CNN&DBN was established to extract music score. Experiments show that the proposed model in a variety of different types of polyphony music recognition showed more accurate recognition and good performance; the recognition rate of the improved algorithm applied to the soundtrack identification is as high as 98.4%, which is significantly better than those of other classic algorithms, proving that CNN&DBN can achieve better effect in music information retrieval. It provides data support for constructing knowledge graph in music field and indicates that deep learning has great research value in music retrieval field.

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Artifact removal from EEG signals recorded in non-restricted environment

Cited by 17 publications

References 32 publications

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

Rede Neural Convolucional e LSTM para Biometria Baseada em EEG no Modo de Identificação

Music Score Recognition Method Based on Deep Learning

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