ECG signal classification and emotion predictions
By: Aswin, T. T
.
Contributor(s): Kumar, Rashmi Rekha.
Publisher: Haryana IOSR - International Organization of Scientific Research 2022Edition: Vol.24(4), Jul-Aug.Description: 8-21p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: An electroencephalogram (EEG) is an experiment to determine the activity of electrical brain signals using
small, metal electrodes which are affixed to the brain scalp. These EEG signals are one of most complex signals
that represents the brain activity. Dataset is collected from the Physionetdata-base which comprises the motor
Imagery functions of Left and right fist, both fists and both feet. These recordings might be distorted by the
contamination of traces such as blinking of eyes or muscle movements etc. In this paper, the method called the
common spatial patterns (CSP) has been implemented as feature extraction technique. This CSP technique
includes one-hot encoding and z-score normalization. CSP is the characteristic eradication approach with the
usage of spatial filtering technique that differentiates the EEG signalsof two classes. One-hot encoding is used
to transform categorical statistics into integer information. The Deep learning technique used for classification
is two-Dimensional convolutional neural network (CNN). I have got the training accuracy of 93.8% and the
validation accuracy of 92.9%.
Using the Emotions.csv dataset going to predict the emotional state of a subject given their EEG readings while
watching various movie scenes. In the program using tensorflow Recurrent Neural Network to make our
predictions. RNN’s can use their internal state (memory) to process variable length sequences of inputs. This
makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Recurrent Neural Networks are theoretically Turing complete and can run arbitrary programs to process
arbitrary sequences of inputs. After that, giving it to model for prediction also evaluating it using the confusion
matrix. I have got Test Accuracy as 96.56%.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2022-2011 |
An electroencephalogram (EEG) is an experiment to determine the activity of electrical brain signals using
small, metal electrodes which are affixed to the brain scalp. These EEG signals are one of most complex signals
that represents the brain activity. Dataset is collected from the Physionetdata-base which comprises the motor
Imagery functions of Left and right fist, both fists and both feet. These recordings might be distorted by the
contamination of traces such as blinking of eyes or muscle movements etc. In this paper, the method called the
common spatial patterns (CSP) has been implemented as feature extraction technique. This CSP technique
includes one-hot encoding and z-score normalization. CSP is the characteristic eradication approach with the
usage of spatial filtering technique that differentiates the EEG signalsof two classes. One-hot encoding is used
to transform categorical statistics into integer information. The Deep learning technique used for classification
is two-Dimensional convolutional neural network (CNN). I have got the training accuracy of 93.8% and the
validation accuracy of 92.9%.
Using the Emotions.csv dataset going to predict the emotional state of a subject given their EEG readings while
watching various movie scenes. In the program using tensorflow Recurrent Neural Network to make our
predictions. RNN’s can use their internal state (memory) to process variable length sequences of inputs. This
makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Recurrent Neural Networks are theoretically Turing complete and can run arbitrary programs to process
arbitrary sequences of inputs. After that, giving it to model for prediction also evaluating it using the confusion
matrix. I have got Test Accuracy as 96.56%.
Click on an image to view it in the image viewer
There are no comments for this item.