In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.
Human drowsiness refers to a physiological state of reduced mental or physical performance resulting from insufficient sleep, long duty periods or irregular work hours. It is attributed to millions of car crashes [
Conventional optical methods for detecting fatigued driving are unreliably sensitive to lighting conditions [
The main goal of this paper is to design a new polling algorithm for real-time determination of human drowsiness via an affordable brain-computer interface with the following features:
1) Real-time data acquisition: data sampling window is controlled within one second for computing drowsiness.
2) Real-time data processing: execution time of the algorithm alone is controlled within one millisecond.
3) General algorithm: a general-purposed polling formula is designed and is approximately invariant with test subjects. It does not need the tuning from training data, overcoming a major drawback of existing methods.
4) Affordability: the brain-computer interface is controlled under $200, excluding the cost of computer.
The rest of this paper is organized as follows. In Section 2, the materials and methods related to this study are provided. Next, experimental and computing results are given in Section 3 together with discussion. In Section 4, some conclusions and future work are presented.
In this study, two types of brain-computer interface devices were used:
1) A product from Open BCI (data sampling rate: 250 Hz).
2) A product from Emotiv (data sampling rate: 128 Hz).
Both companies sell products under $200, which are affordable to regular users, as shown in
In details, the following main materials were used, as shown in
1) Gold-coated electrodes and conductive paste (Open BCI).
2) A data acquisition board (Open BCI).
3) A data wireless receiver (Open BCI).
4) A battery power unity (Open BCI).
5) Integrated electrodes and a data acquisition unit (Emotiv).
6) A data wireless receiver (Emotiv).
7) A bottle of all-purpose saline solution (Emotiv).
The outline of our approach is given in
10 - 20 system is an internationally recognized method to specify the location of scalp electrodes for electroencephalography (EEG) test [
Brain waves contain much random information that is difficult to be analyzed in time domain (
・
・
・
・ Euler’s formula:
・
In frequency domain, the norm of different frequency bands (delta, theta, alpha, beta and gamma) is defined as biometric markers for drowsiness:
where
A general-purpose polling formula was designed to predict the human drowsiness:
1) Traditionally, the drowsiness is quantified by a binary variable: Yes or No, which is not accurate enough to describe such a complex process. A real number variable, depth of drowsiness, is proposed to precisely describe the different levels of drowsiness (
2) In computer graphics [
3) In sleep science, different frequency channels are often used to investigate sleep patterns. In signal processing, Fourier transform [
4) Inspired by political voting, a new polling scheme, i.e., judging the drowsiness on the basis of the vote of frequency bands, is designed to express the relation between the depth of drowsiness and the norm of frequency bands:
where Nδ, Nθ, Nα, Nβ, Nγ are normalized band norms for δ, θ, α, β, and γ channels, respectively.
The derivation of the above formula is given in Appendix A.
For calibrating the sampling rate of the hardware, A stopwatch and the data acquisition software of the devices were used.
A Human Participant Form was approved by a local IRB in 2015 and each participant gave his/her permission before the experiment. Ten human subjects were measured with names replaced by pseudo identification numbers, with four of them tested only in waking states. The main test steps of this study are:
1) Assembling and calibration of the brain-computer interface devices.
2) Design and development of the biometric algorithm (i.e., a polling scheme) as well as the setup of the brain-computer interface device.
3) Collection of brain wave data by wearing a brain-computer interface device. Measurement last for about 6 - 7 seconds at a sampling frequency (Fs = 250 Hz). Drowsy state: a) Test time was chosen around midnight; b) Subjects were asked to click the “Data Collection” button once they felt drowsy. Waking state: a) Test time was chosen during daytime; b) Severe movement was avoided.
4) Inquiry of observed depth of drowsiness from test subjects. Drowsy stage: Extremely sleepy (0.95), Very sleepy (0.8), Moderately sleepy (0.65), Lightly sleepy (0.5).Waking stage: Extremely alert (0.05), Very alert (0.2), Moderately alert (0.35), Lightly alert (0.45).
Time(second) | Case 1 | Case 2 | Case 3 | Case 4 | Case 5 |
---|---|---|---|---|---|
Stopwatch | 5.10 | 5.08 | 4.98 | 5.01 | 4.95 |
Acquisition Software | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 |
Sampling points | Time (second) | Sampling points | Time (second) | Sampling points | Time (second) |
---|---|---|---|---|---|
0 | 0 | 22 | 0.171875 | 44 | 0.34375 |
1 | 0.007813 | 23 | 0.179688 | 45 | 0.351563 |
2 | 0.015625 | 24 | 0.1875 | 46 | 0.359375 |
3 | 0.023438 | 25 | 0.195313 | 47 | 0.367188 |
4 | 0.03125 | 26 | 0.203125 | 48 | 0.375 |
5 | 0.039063 | 27 | 0.210938 | 49 | 0.382813 |
6 | 0.046875 | 28 | 0.21875 | 50 | 0.390625 |
7 | 0.054688 | 29 | 0.226563 | 51 | 0.398438 |
8 | 0.0625 | 30 | 0.234375 | 52 | 0.40625 |
9 | 0.070313 | 31 | 0.242188 | 53 | 0.414063 |
10 | 0.078125 | 32 | 0.25 | 54 | 0.421875 |
11 | 0.085938 | 33 | 0.257813 | 55 | 0.429688 |
12 | 0.09375 | 34 | 0.265625 | 56 | 0.4375 |
13 | 0.101563 | 35 | 0.273438 | 57 | 0.445313 |
14 | 0.109375 | 36 | 0.28125 | 58 | 0.453125 |
15 | 0.117188 | 37 | 0.289063 | 59 | 0.460938 |
16 | 0.125 | 38 | 0.296875 | 60 | 0.46875 |
17 | 0.132813 | 39 | 0.304688 | 61 | 0.476563 |
18 | 0.140625 | 40 | 0.3125 | 62 | 0.484375 |
19 | 0.148438 | 41 | 0.320313 | 63 | 0.492188 |
20 | 0.15625 | 42 | 0.328125 | 64 | 0.5 |
21 | 0.164063 | 43 | 0.335938 |
5) Conversion of brain data to MATLAB data format and execution of the designed polling algorithm for predicting the depth of drowsiness.
6) Analysis and validation of the algorithm.
As shown in
Our biometric markers for drowsiness are roughly invariant between C4 and P3 channels, as illustrated in
stringent requirement on the location of electrodes on human scalp. It is difficult to put a sensor at the exact location of a particular channel specified by the 10 - 20 electrode system.
The depth of drowsiness varies more severely in waking stage than in drowsy stage. This may be due to the fact that test subjects are more likely influenced from environment during the waking stage. Even with the variation in the waking stage, there is a clear-cut between the two stages, as shown in
The execution time of the MATLAB code is within 1 millisecond (not including the data acquisition time, which is 1 second for each estimate), as listed in
The computed depth of drowsiness is also generally invariant from ages of test subjects, as illustrated in
The following concluding remarks can be drawn from this study:
1) In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description.
Test Cases | Inquired State | Computed Depth of Drowsiness | Computed State (our method) | Alpha/Beta | Computed State (existing method) |
---|---|---|---|---|---|
4.1 | w | 0.27 | w | 0.044 | w |
4.2 | w | 0.63 | w | 0.042 | w |
4.3 | w | 0.33 | w | 0.0167 | w |
4.4 | w | 0.59 | w | 0.044 | w |
4.5 | w | 0.23 | w | 0.028 | w |
5.1 | d | 0.82 | d | 0.71 | d |
5.2 | d | 0.81 | d | 1.01 | d |
5.3 | d | 0.65 | w | 0.71 | d |
5.4 | d | 0.66 | d | 0.57 | d |
---|---|---|---|---|---|
5.5 | d | 0.78 | d | 0.78 | d |
5.6 | d | 0.82 | d | 0.45 | w |
5.7 | d | 0.65 | w | 0.72 | d |
5.8 | d | 0.74 | d | 0.65 | d |
5.9 | d | 0.77 | d | 0.68 | d |
5.11 | d | 0.77 | d | 0.73 | d |
4.6 | w | 0.22 | w | 0.041 | w |
4.7 | w | 0.64 | w | 0.04 | w |
4.8 | w | 0.42 | w | 0.01 | w |
4.9 | w | 0.52 | w | 0.05 | w |
4.11 | w | 0.12 | w | 0.03 | w |
7.1 | d | 0.83 | d | 1.79 | d |
7.2 | d | 0.77 | d | 0.98 | d |
7.3 | d | 0.52 | w | 0.63 | d |
7.4 | d | 0.78 | d | 0.53 | d |
7.5 | d | 0.83 | d | 0.61 | d |
7.6 | d | 0.68 | d | 0.82 | d |
7.7 | d | 0.72 | d | 0.62 | d |
7.8 | d | 0.84 | d | 0.62 | d |
7.9 | d | 0.84 | d | 0.73 | d |
7.11 | d | 0.68 | d | 0.65 | d |
8.1 | w | 0.64 | w | 0.84 | d |
8.2 | w | 0.3 | w | 0.61 | d |
8.3 | w | 0.26 | w | 0.77 | d |
8.4 | w | 0.67 | d | 0.98 | d |
8.5 | w | 0.67 | d | 1.03 | d |
8.6 | w | 0.6 | w | 0.78 | d |
8.7 | w | 0.32 | w | 0.59 | d |
8.8 | w | 0.52 | w | 0.44 | w |
8.9 | w | 0.66 | d | 0.43 | w |
8.11 | w | 0.64 | w | 0.54 | d |
17.1 | w | 0.24 | w | 0.89 | d |
17.2 | w | 0.62 | w | 0.29 | w |
17.3 | w | 0.66 | d | 0.29 | w |
17.4 | w | 0.45 | w | 0.28 | w |
17.5 | w | 0.41 | w | 0.28 | w |
17.6 | w | 0.36 | w | 1.02 | d |
17.7 | w | 0.27 | w | 0.98 | d |
17.8 | w | 0.66 | d | 0.89 | d |
17.9 | w | 0.7 | d | 1.23 | d |
17.11 | w | 0.58 | w | 1.11 | d |
Items | Our method | Existing method [ |
---|---|---|
Success cases | 41 | 35 |
Failure cases | 9 | 15 |
Accuracy (%) | 82 | 70 |
2) After many attempts, we found a set of effective biometric markers for drowsiness: normalized band norms. These markers do not change with scaling the voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices.
3) We designed and implemented a new polling algorithm for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications.
4) Test results show that the depth of drowsiness computed by the new method is generally invariant from ages of test subjects and sensor channels (P3 and C4). This eliminates the need of training data required by existing methods. Therefore, our method is better suited to a random driver than existing methods.
5) The cost of the brain-computer interface devices (not including the computer) can be under $200, which is affordable to regular users.
6) In comparison with a recent study [
Possible future work may include:
1) Apply the method to a large-scale investigation.
2) Investigate an optimal way to place scalp electrodes.
3) Test the impact of environment such as vehicle vibration.
4) Extend our polling algorithm to include signals from electromyography (EMG) and electrocardiogram (EKG or ECG).
5) Study on alcohol and drug influence, blackout, road rage, and medical emergency.
Shen, J., Li, B.Y. and Shi, X.F. (2017) Real-Time Detection of Human Drowsiness via a Portable Brain- Computer Interface. Open Journal of Applied Sciences, 7, 98-113. https://doi.org/10.4236/ojapps.2017.73009
1) First let sleep ® 1.0 and waking ® −1.0
2) Use a linear relation to represent
where Nδ and Nβ are normalized frequency bands for δ and β channels, as defined in Equation (3). cδ and cβ are two coefficients.
3) From prior knowledge in sleep science, δ channel contributes to sleep, while β channel contributes to the waking stage. Then, we have
4) Expand the contributions of θ and α channels:
where Nθ and Nα are defined in Equation (3). Assume cθ = 0.5 and cα = −0.5 for their corresponding contributions to waking and sleep stages, respectively.
5) Consider Nγ’s contribution to the waking stage, we rewrite Equation (A2) as
6) Map
where
By solving the above two equations, we have
Thus,
Theorem: The computed depth of drowsiness is invariant from scaling the amplitude of brain waves.
Proof:
Given a sequence of N sample
where
The substitution of
By using
By using
Since
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