Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data a...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/428 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587474582372352 |
|---|---|
| author | M. N. Afzal Khan Nada Zahour Usman Tariq Ghinwa Masri Ismat F. Almadani Hasan Al-Nashah |
| author_facet | M. N. Afzal Khan Nada Zahour Usman Tariq Ghinwa Masri Ismat F. Almadani Hasan Al-Nashah |
| author_sort | M. N. Afzal Khan |
| collection | DOAJ |
| description | Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge. In this study, we employ a classification strategy to explore stress and its impact on spatial activation patterns and brain connectivity caused by the Stroop color–word task (SCWT). To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). The study is carried out at two separate times of day (morning and evening) and involves 21 healthy participants. Additionally, we introduce binaural beats (BBs) stimulation to investigate its potential for stress reduction. The morning session includes a control phase with 10 SCWT trials, whereas the afternoon session is divided into three phases: stress, mitigation (with 16 Hz BB stimulation), and post-mitigation, each with 10 SCWT trials. For a comprehensive evaluation, the acquired fNIRS data are classified using a variety of machine-learning approaches. Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. Notably, after augmenting the data with DCGAN, the classification accuracy increases dramatically to 96%. In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. These findings illustrate the ability to detect changes in brain states with high accuracy using fNIRS, underline the need for larger datasets, and demonstrate that data augmentation can significantly help when data are scarce in the case of brain signals. |
| format | Article |
| id | doaj-art-0675472a9c634a8185bf2246325f5ecb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0675472a9c634a8185bf2246325f5ecb2025-01-24T13:48:53ZengMDPI AGSensors1424-82202025-01-0125242810.3390/s25020428Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRSM. N. Afzal Khan0Nada Zahour1Usman Tariq2Ghinwa Masri3Ismat F. Almadani4Hasan Al-Nashah5Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab EmiratesAccurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge. In this study, we employ a classification strategy to explore stress and its impact on spatial activation patterns and brain connectivity caused by the Stroop color–word task (SCWT). To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). The study is carried out at two separate times of day (morning and evening) and involves 21 healthy participants. Additionally, we introduce binaural beats (BBs) stimulation to investigate its potential for stress reduction. The morning session includes a control phase with 10 SCWT trials, whereas the afternoon session is divided into three phases: stress, mitigation (with 16 Hz BB stimulation), and post-mitigation, each with 10 SCWT trials. For a comprehensive evaluation, the acquired fNIRS data are classified using a variety of machine-learning approaches. Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. Notably, after augmenting the data with DCGAN, the classification accuracy increases dramatically to 96%. In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. These findings illustrate the ability to detect changes in brain states with high accuracy using fNIRS, underline the need for larger datasets, and demonstrate that data augmentation can significantly help when data are scarce in the case of brain signals.https://www.mdpi.com/1424-8220/25/2/428functional near-infrared spectroscopy (fNIRS)hemodynamic responsedeep convolutional generative adversarial network (DCGAN)feed-forward neural networklinear support vector machinesdecision tree |
| spellingShingle | M. N. Afzal Khan Nada Zahour Usman Tariq Ghinwa Masri Ismat F. Almadani Hasan Al-Nashah Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS Sensors functional near-infrared spectroscopy (fNIRS) hemodynamic response deep convolutional generative adversarial network (DCGAN) feed-forward neural network linear support vector machines decision tree |
| title | Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS |
| title_full | Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS |
| title_fullStr | Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS |
| title_full_unstemmed | Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS |
| title_short | Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS |
| title_sort | exploring effects of mental stress with data augmentation and classification using fnirs |
| topic | functional near-infrared spectroscopy (fNIRS) hemodynamic response deep convolutional generative adversarial network (DCGAN) feed-forward neural network linear support vector machines decision tree |
| url | https://www.mdpi.com/1424-8220/25/2/428 |
| work_keys_str_mv | AT mnafzalkhan exploringeffectsofmentalstresswithdataaugmentationandclassificationusingfnirs AT nadazahour exploringeffectsofmentalstresswithdataaugmentationandclassificationusingfnirs AT usmantariq exploringeffectsofmentalstresswithdataaugmentationandclassificationusingfnirs AT ghinwamasri exploringeffectsofmentalstresswithdataaugmentationandclassificationusingfnirs AT ismatfalmadani exploringeffectsofmentalstresswithdataaugmentationandclassificationusingfnirs AT hasanalnashah exploringeffectsofmentalstresswithdataaugmentationandclassificationusingfnirs |