EEG based stress level identification

Guo, Jun; Smitha, K. G.

doi:10.1109/smc.2016.7844738

Cited by 119 publications

(76 citation statements)

References 13 publications

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“…Considering the limitation in use of fNIRS signal in the previous researcher, for better comparison, we have used studies in which EEG signal has been used for stress detection. Jun et al 45 and Smitha et al 25 using EEG signal, which has been recorded as 14 channels, the classification accuracy of the inactive state (rest) and active state (stress) has been 96 and 85.17%, respectively. In this research, only using fNIRS signal, the results obtained from the active and inactive states classification are more superior than the findings in other research.…”

Section: Resultsmentioning

confidence: 99%

“…Jun et al 45 induced high and low stress levels in the volunteers using mathematical and stroop task. During the task, EEG signals were recorded as 14 channels, and based on the features extracted from the recorded signals, the best result in classification between low and high stress levels has been 75%.…”

Section: Resultsmentioning

confidence: 99%

“…During the task, EEG signals were recorded as 14 channels, and based on the features extracted from the recorded signals, the best result in classification between low and high stress levels has been 75%. Hou et al 46 acted in the same way as Jun et al 45 in terms of signal recording and the stress task and the best result in classifying high and low stress levels 47 the stress task has been based on driving in the real world, which induces stress at high and low levels. In that research, ECG, EMG, GSR, and respiration signals were recorded during the task.…”

Section: Resultsmentioning

confidence: 99%

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Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Shirvan

Setarehdan

Nasrabadi

2018

Int Clin Neurosci J

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Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health. Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination. Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states. Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Shirvan

Setarehdan

Nasrabadi

2018

Int Clin Neurosci J

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“…The second limitation relates to the precision of the headset to measure the patient’s stress. While this may seem like a major limitation, there are studies that have determined the accuracy of this sensor can be up to 88–96% when using a two-level classification [ 37 ]. In addition, to reduce any possible erroneous readings provided by the headset, an algorithm is used to smooth any abrupt changes in the readings.…”

Section: Sensors and Actuatorsmentioning

confidence: 99%

Ambient Intelligence Environment for Home Cognitive Telerehabilitation

Oliver

Teruel

Molina

et al. 2018

Sensors

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Higher life expectancy is increasing the number of age-related cognitive impairment cases. It is also relevant, as some authors claim, that physical exercise may be considered as an adjunctive therapy to improve cognition and memory after strokes. Thus, the integration of physical and cognitive therapies could offer potential benefits. In addition, in general these therapies are usually considered boring, so it is important to include some features that improve the motivation of patients. As a result, computer-assisted cognitive rehabilitation systems and serious games for health are more and more present. In order to achieve a continuous, efficient and sustainable rehabilitation of patients, they will have to be carried out as part of the rehabilitation in their own home. However, current home systems lack the therapist’s presence, and this leads to two major challenges for such systems. First, they need sensors and actuators that compensate for the absence of the therapist’s eyes and hands. Second, the system needs to capture and apply the therapist’s expertise. With this aim, and based on our previous proposals, we propose an ambient intelligence environment for cognitive rehabilitation at home, combining physical and cognitive activities, by implementing a Fuzzy Inference System (FIS) that gathers, as far as possible, the knowledge of a rehabilitation expert. Moreover, smart sensors and actuators will attempt to make up for the absence of the therapist. Furthermore, the proposed system will feature a remote monitoring tool, so that the therapist can supervise the patients’ exercises. Finally, an evaluation will be presented where experts in the rehabilitation field showed their satisfaction with the proposed system.

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“…Both autonomic and neural responses can distinguish between affective and neutral stimuli across visual and auditory modalities [11], [12], [13], [14] and autonomic and neural responses can both identify stressful situations [15], [16], [17], [18]. Therefore, multimodal synchrony assessment, based on both neural and autonomic channels, could lead to more robust detection of emotionally or cognitively relevant events.…”

Section: Introductionmentioning

confidence: 99%