Motor Imaginary EEG Signals Classification Based on Deep Learning

Wang, Haoran; Mo, Wanying

doi:10.1007/978-981-13-2826-8_13

Cited by 1 publication

(1 citation statement)

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“…Some researches have explored the use of LSTM on non-medical EEG-based applications. Most of the EEG-based LSTM applications were used in brain-computer interface (BCI), such as motor imagery classification [ 40 , 41 , 42 , 43 , 44 , 45 , 46 ], emotion classification [ 47 , 48 , 49 , 50 , 51 , 52 ], depression detection [ 53 , 54 , 55 ], biometrics [ 56 , 57 ], sleep stage classification [ 58 , 59 , 60 , 61 , 62 , 63 ], driving behavioral classification [ 64 , 65 ], directional signal classification [ 66 ], machine health monitoring [ 67 ] and EEG signal classification [ 68 ]. There are some research works on LSTM for medical applications reported in the literature [ 69 , 70 , 71 , 72 , 73 , 74 , 75 ], but as far as our concern, there is still no approach being proposed to identify TBI using LSTM networks.…”

Section: Introductionmentioning

confidence: 99%

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Lai

Ibrahim

Hamid

et al. 2020

Sensors

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Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.

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