|Year : 2018 | Volume
| Issue : 4 | Page : 205-214
A new method for detecting P300 signals by using deep learning: Hyperparameter tuning in high-dimensional space by minimizing nonconvex error function
Seyed Vahab Shojaedini1, Sajedeh Morabbi1, MohammadReza Keyvanpour2
1 Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran
2 Department of Computer Engineering, Alzahra University, Tehran, Iran
|Date of Submission||17-Feb-2018|
|Date of Acceptance||01-Jun-2018|
|Date of Web Publication||13-Sep-2019|
Dr. Seyed Vahab Shojaedini
Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran
Source of Support: None, Conflict of Interest: None
Background: P300 signal detection is an essential problem in many fields of Brain-Computer Interface (BCI) systems. Although deep neural networks have almost ubiquitously used in P300 detection, in such networks, increasing the number of dimensions leads to growth ratio of saddle points to local minimums. This phenomenon results in slow convergence in deep neural network. Hyperparameter tuning is one of the approaches in deep learning, which leads to fast convergence because of its ability to find better local minimums. In this paper, a new adaptive hyperparameter tuning method is proposed to improve training of Convolutional Neural Networks (CNNs). Methods: The aim of this paper is to introduce a novel method to improve the performance of deep neural networks in P300 signal detection. To reach this purpose, the proposed method transferred the non-convex error function of CNN) into Lagranging paradigm, then, Newton and dual active set techniques are utilized for hyperparameter tuning in order to minimize error of objective function in high dimensional space of CNN. Results: The proposed method was implemented on MATLAB 2017 package and its performance was evaluated on dataset of Ecole Polytechnique Fédérale de Lausanne (EPFL) BCI group. The obtained results depicted that the proposed method detected the P300 signals with 95.34% classification accuracy in parallel with high True Positive Rate (i.e., 92.9%) and low False Positive Rate (i.e., 0.77%). Conclusions: To estimate the performance of the proposed method, the achieved results were compared with the results of Naive Hyperparameter (NHP) tuning method. The comparisons depicted the superiority of the proposed method against its alternative, in such way that the best accuracy by using the proposed method was 6.44%, better than the accuracy of the alternative method.
Keywords: Brain–computer interface, deep neural network, hyperparameter, nonconvex error function, P300 signal
|How to cite this article:|
Shojaedini SV, Morabbi S, Keyvanpour M. A new method for detecting P300 signals by using deep learning: Hyperparameter tuning in high-dimensional space by minimizing nonconvex error function. J Med Signals Sens 2018;8:205-14
|How to cite this URL:|
Shojaedini SV, Morabbi S, Keyvanpour M. A new method for detecting P300 signals by using deep learning: Hyperparameter tuning in high-dimensional space by minimizing nonconvex error function. J Med Signals Sens [serial online] 2018 [cited 2021 Jan 28];8:205-14. Available from: https://www.jmssjournal.net/text.asp?2018/8/4/205/246741
| Introduction|| |
Recently, brain–computer interface (BCI) technology has had a vast and rapid growth in control outer equipment using event-related potential (ERP) signals. These signals are produced in the human brain as response to external stimulus having great potential to make a nonmuscles communication path between disabled people and outside world.,
One of the important ERPs is P300 signal which is applied in BCIs, diagnosis of neurological disorders and lie detection., The P300 waveform evoked between 250 and 300 ms after a brief auditory or visual stimulus. Due to its wide applications, detecting of P300 is still a serious problem in BCI paradigm. Furthermore, low signal-to-noise ratio (SNR) makes this problem open and so challenging.
In general, detecting of P300 signal consists of three steps. The first step is preprocessing in which some useless features of the signal are removed. In the second step, discriminative features are extracted from signal, and finally in the third step, classification makes up a model to distinguish P300 and non-P300 components. Based on the above procedure, effective feature extraction and classification methods make great impact on increase performance of the P300 detection.
Several techniques have been applied to improve P300 detection. Averaging is the oldest method, which tries to obtain higher detection rate by increasing the SNR. However, this idea reduces the bit rate in P300 and may deform the ERP signal., Artificial intelligence methods have been proposed to increase SNR without loss of any considerable information in P300 detection.,
A group of investigations has depicted the application of linear and nonlinear models for P300 detection and classification.,,, For instance, linear discriminant analysis and support vector machines have extensively been applied in BCI applications.,,
Certainly, linear techniques are not enough strong to deal with complex real-world problems and their nonlinear counterparts have faced with overfitting. One of the most important techniques to detect P300 is artificial neural network.,, Although usefulness of this idea, its basic drawback is getting stuck in local minimum which degrades its performance in P300 detection. In recent years, studies have demonstrated the potential of deep neural networks in the field of P300 detection. One of the most popular types of deep neural networks is convolutional neural network (CNN) and recurrent CNN (RCNN) which have been widely used for P300 detection, thanks to their ability in extracting high-level features., Based on the literature, RCNN is used to learn electroencephalography (EEG) signals for mental activity classification. Furthermore, the RCNNs successfully preserved the spectral, spatial, and temporal structure of the data during classification. In another research, spatial and temporal features of the EEG signal have been combined to train a two-dimensional CNN. Furthermore, three-dimensional CNNs were used to preserve spatiotemporal features and also employ transfer learning to further increase classification performance.
Another type of method employs EEG data in time and space domains in order to offer better results regardless of the number of layers of CNN. In another group of researches, new CNN architectures were proposed which used a depthwise and separable convolution to more efficiently extract relevant features for EEG-based BCIs. It has been shown that such techniques resulted in more accurate classification in parallel with being as compact as possible. Deep neural networks have a deep structure with multiple levels of data representations. However, they have some drawbacks mainly containing the increase in dimensions of the deep neural network, which leads to higher process volume and the ratio of the number of saddle points to local minima increases exponentially.,
Such saddle points are enclosed by some high error plateaus that may extremely slow down learning process and give the illusory impression of the existing local minimum. This phenomenon seriously hampers the detection of P300 signals. Therefore, escaping from saddle points has been acquainted as a vital challenge in P300 detection by deep neural networks. It is NP-hard problem, therefore, as a solution minimizing nonconvex error function in high-dimensional spaces may be demonstrated.
A vast variety of methods have been proposed to escape saddle points containing the first order, second order, and evolutionary algorithms. The first-order algorithms try to improve P300 detection using gradient information. They are simple to use and converge fast., The second-order algorithms compute Hessian matrix, which highly depends on dimensions of objective function. As the dimensions grow, the required memory also increases., In some researches, evolutionary approaches have been used as learning schemes for deep neural networks. Certainly, these methods have high volume of process and computational complexity in each generation.
The stochastic gradient descent (SGD) family is a de facto optimization paradigm for tuning a deep architecture. A critical problem in SGD is to set hyperparameters seriously influences the convergence and the performance of the deep neural network. Adaptive methods may improve the hyperparameters efficiently which lead to obtain higher performance in parallel with speed up training process.
In this paper, a novel method is introduced to improve training of deep neural networks, which is based on adaptive hyperparameter tuning. The proposed method minimizes nonconvex error function in high-dimensional space. For this purpose, the objective function is transferred to Lagrangian paradigm as a two-constrained optimization problem including either quality or inequality constraints. Then, Karush–Kuhn–Tucker (KKT) system is used to translate the problem into a standard nonlinear framework. Finally, the hyperparameter tuning is achieved using iterative Newton and dual active set techniques. The proposed method is applied on Adadelta module of a CNN to obtain a well-fit model for distinguishing P300 and non-P300 signals.
The structure of the paper is as follows: Section 2 includes description of dataset and proposed protocol. In Section 3, the results of evaluating the proposed method against its alternatives are demonstrated. In Section 4, the obtained results are compared to the state-of-the-art method using some effective indexes. The conclusion is presented in the last section of the paper.
| Materials and Methods|| |
In this section, firstly the details about the utilized dataset and preprocessing techniques were described. Then, the proposed scheme is introduced to address saddle point problem using a new optimal hyperparameter tuner. Finally, the proposed technique is applied on CNN to improve P300 detection.
In this paper, EPFL BCI dataset was applied. It has been captured by the Biosemi system with 32 electrodes located according to standard 10-20 international system position at 2048 HZ. The EPFL BCI dataset is composed of eight available subjects as its specifications have been represented in [Table 1]. The data of each subject is composed of four sessions. Each of the sessions consisted of six runs; they are corresponding to six images which had displayed in a six-cell paradigm.
The images were flashed at random order. Each flash of an image lasted for 100 ms and then during 300 ms none of them were flashed. In a six-cell paradigm, the interstimulus interval (ISI) was 400 ms. More details about the dataset may be found in.
An overview of the proposed protocol is shown in [Figure 1] which consists of five stages which are illustrated as below.
The first stage is data acquisition, during this step, the raw EEG signals were captured using 32 electrodes which are located on the scalp.
Before learning deep model, preprocessing steps were applied. This stage was composed of six successive steps which are described as bellow.
The EEG voltages have been recorded using 32 electrodes which have depended on each other. Hence, activities in the reference electrode may be reflected in the recorded signal of others. Referencing method used to remove such anomaly effects in the activity of electrodes.
A sixth-order forward–backward Butterworth bandpass filter was used at 1.0 and 12.0 Hz cutoff signal frequency, to remove additional frequencies and noise.
The EEG was downsampled from 2048 to 32 Hz by selecting each 64th sample from the bandpass-filtered data.
Signal trial extraction
Single trials of duration 1000 ms were extracted from the data. These trials were started at stimulus onset, that is, at the beginning of the intensification of an image and were ended 1000 ms after stimulus onset. Due to the ISI of 400 ms, the last 600 ms of each trial overlapped with the last 600 ms of the following trial.
To reduce the effects of outliers in EEG signal such as muscles activity and eye blinks, the 10th percentile and the 90th percentile of data of each electrode were computed. Amplitude values lying out of this range were replaced by start and end limits of this range, respectively.
Normalizing is a way to control behavior of data in the model. The last stage of preprocessing is mapping the EEG signal to the range of (0, 1) to set data onto the same range, so the computational complexity has a sharp decrease.
Learning deep model
Deep CNNs have been based on deep learning theory which has large scale and different types of layers. A portion of this structure is responsible for extracting discriminative features of input data, while others are responsible for classification the data based on extracted features. Therefore, deep architecture leads to better data generalization and representation thanks to its complex structure which provides the capability of either feature extraction or classification. The basic part of the deep CNN is convolutional layer. This layer contains a collection of filter banks which are applied to input data to extracts various features. Furthermore, a nonlinear activation function is applied on neurons. Then, the pooling, which is also known as a downsampling layer is employed. Finally, the resultant features were assigned to the last layer (i.e., fully connected) in order to classify the data. Several candidates (for example, Adadelta, Adam, RMSprop) have been proposed to perform the above classification.
Suppose ω as Adadelta model parameters and T (ω) as its objective function which may be minimized to obtain a well-fit model. Gradient descent procedure is a way to obtain this optimization which makes use of the gradient information of the objective function to update model parameters as bellow:
In E(∇ω2)t which shows running average over the gradient of the squared model's parameter which is only depends on previous average and the current gradient. The parameter α refers to momentum and the optimization criteria for Adadelta error may be written as a set of qualities and inequalities as below:
Where X denotes the distance between the current model and local quadratic approximation's minimum, A denotes the estimate for gradient variance, furthermore, kmax and kmin refer to the maximum and minimum generalized curvature.,, To obtain the well-fit model, it is necessary to minimize the nonlinear j (α) (i.e., Eq. 2) subject to equality constraint P1 and inequality constraint P2. To perform such optimization first supposes quality constraint of Eq. 2 as:
Putting the mentioned equation in Lagrangian paradigm leads to:
Where y denotes the Lagrangian multiplier. To achieve optimum parameter, the KKT system is written in the form of nonlinear equations as follows:
Let us write this system of equations more compactly as H (α, y) = 0:
Our target is to solve Eq. 6 using Newton paradigm, therefore, the gradient of H (α, y) is computed as:
In Eq. 7, is the Hessian of L(α, y) and is defined as:
Based on the symmetry of ∇H(α, y) which leads to J(α, y) = ∇H(α, y)T = ∇H(α, y), Newton's method (i.e., Eq. 7) gives
The above equation may be rewritten in below quadratic form to find and the optimum (∇αT,∇yT):
Now Eq. 9 is rewritten in a simpler form, by replacing ∇y with ϑ−y as:
The above equation may be arranged as:
Combining Eqs. 9 and 12 may result in Eq. 13, as follows:
Therefore, the quadratic Eq. 10 may be commented as:
Furthermore, the explained process is extended to include inequality constraint in Eq. 2 which results in bellow equation:
In a similar manner with Eqs 2–14, the Eq. 15 leads to 16 which is quadratic form of inequality constraint (i.e., similar to Eq. 14):
Now we have simplified Eqs. 14 and 16 which solving them leads to the optimization of Eq. 2 in such way that the optimal value of the hyperparameter is achieved. A sequence of Newton iterations is applied to find an acceptable solution for Eq. 14. At every iteration, the direction improving is performed; therefore, the process is called sequential quadratic programming. To solve Eq. 16, the active set or dual active set method is used to obtain the optimal solution.
Detection and classification
A pretrained CNN is applied to detect P300 signals. The CNN builds up a model to map between the features and EEG signals categories, which are known as P300 and non-P300 with binary labels. A comprehensive pseudocode of the proposed P300 detection protocol is shown in [Figure 2].
| Results|| |
The proposed method was implemented on MATLAB R2017a, with an Intel Core i7 and 2 TB RAM. It was applied on EPFL dataset which was recorded using 32 channels from 8 subjects. The electrodes were located in predetermined positions based on 10–20 system, as shown in [Figure 3].
|Figure 3: The position of 32 electrodes based on international 10–20 system|
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The data of each subject composed of four sessions. Hence, in each subject, the data from two sessions were used to train and one another used to validation and the data from leave-off session was used to test. This method was repeated four times, hence, each session was presented once for test. At least, for each subject, the average of four steps at four different examined methods was evaluated.
The EEG data of each subject were first preprocessed to make it ready to feed up to CNN. Then, P300 was detected by applying CNN which had been trained by Adadelta based Naive Hyperparameter Tunning which is called as NHP for brevity in the rest of article, Adam, RMSprop, and proposed schemes, respectively. The structure of CNN is presented in [Figure 4]. As it illustrated, the CNN composed of four convolutional layers including two max pooling and two activation function layers. In the above and bottom of each layer, some information about the current layer is presented. For example, in the above of the first convolutional layer, the phrase Conv (5, 1, 0, and 20) means that in this layer, a 5 by 5 filter, stride size 1, no padding, and 20 neurons are used. Therefore, the output size of the current layer is 28 by 28 by 20 by 30 as indicated below of this layer. The phrase 28 × 28 × 20 × 30 in the bottom of layer mentioned that a linear product using the first layer is applied as the input of CNN. Therefore, the size of input changed to 28 by 28; furthermore, the value 20 refers to the number of neurons and 30 is the batch size in each epoch of learning.
|Figure 4: The architecture of convolutional neural network model for P300 signal detection. In the above of each layer, its name and structure are appeared. (Conv: Convolutional layer, MaxPool: Max pooling layer, FC layer: Fully connected layer and Conv/MaxPool [kernel, stride, padding, neuron]. The arguments in parentheses after the name of layer refer to the size of filter/kernel, the size of stride, the size of padding, and the number of neurons in the current layer, respectively). The below information in each layer indicates the size of the output of that layer|
Click here to view
Finally, to estimate how good they work, their performances were measured. For each subject, fourfold cross-validation was employed to evaluate true-positive ratio (TPR), true-negative ratio (TNR), false-positive ratio (FPR), false-negative ratio (FNR), and classification accuracy. The TP shows the number of correctly identified P300 signals. The TN shows the non-P300 signals which were rejected correctly. The FP is the number of false detections and the FN shows the number of missed P300 signals. The TPR is the ratio of correct detections and may be defined as:
Furthermore, The FNR refers to the ratio of missed P300 signals:
Moreover, the FPR, measured the non-P300 as P300 signals:
The TNR, measured the non-P300 signals which were classified correctly.
The accuracy is defined to measure the state of being correct detection as follows:
They were computed as evaluation parameters which illustrate the effectiveness of the examined approaches as shown in [Table 2].
As [Table 2] shows, based on TPRs, the proposed method outperformed in all of the subjects. For example, the best TPR which has been obtained by this method was equal to 92.90%, over subject 3. However, the best TPRs which have been obtained using NHP, Adam, and RMSprop methods were 78.85%, 83.19%, and 80.55%, respectively, over subject 8. Furthermore, according to TNRs, the proposed method has been better than alternatives. For instance, the proposed method has been obtained TNR of 99.22% over subject 8, which was the best among all TNRs. Whereas, the best TNRs which have been gained by NHP, Adam, and RMSprop methods were 97.85%, 97.41%, and 97.68% over subjects 3, 4, and 2, respectively.
Exploring FPR and FNR values also demonstrated the superiority of the proposed algorithm against its alternatives. By investigating the obtained FPRs, the proposed scheme has been achieved to the value of 0.77% over subject 8, which is the best among all obtained false-positive rates. On the other hand, the best values among alternatives were equal to 2.14%, 2.58%, and 2.31% over subjects 3, 4, and 2, respectively. Moreover, the best obtained FNR was equal to 7.02% over subject 3 using the proposed protocol. However, best FNRs which have been achieved using alternatives (i.e., NHP, Adam, and RMSprop) were equal to 21.14%, 16.80%, and 19.44% over subject 8.
Finally, the classification accuracy was confirmed better performance of our proposed method. The best accuracy has been obtained 95.34% using proposed method over subject 3. However, the best values which have been gained by the alternatives were equal to 88.31%, 89.61%, and 88.78%, over subject 8.
| Discussion|| |
The subjects were faced to a screen on which six images were sequentially displayed including a television, a telephone, a lamp, a door, a window, and a radio. The images were offered in random sequences with a stimulus interval of 400 ms.
Each subject was completed four recording sessions. The first two sessions were performed on one day and the last two sessions on another day. Each of the sessions consisted of six runs, containing one run for each of the six images. As illustrated in [Figure 5], for eight subjects, the averaged data of each run was calculated over all of four sessions. As discussed before, each run is corresponding to each of six images in the specific random order. Hence, each column in presented bar charts refers to how correct an image was detected by special user over all sessions.
|Figure 5: Empirical result of applying the NHP and the proposed methods on Adadelta algorithms for convolutional neural network which is trained on the EPFL dataset based on classification accuracy|
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The given bar charts show the trend of classification accuracy in six runs over eight subjects. As is observed in the column graph, in all cases, the proposed has been better than the best among alternatives based on accuracy (i.e., NHP). The amount of this superiority was various from subjective point of view. At a glance over the proposed method, in subject 1, the significant rise of averaged classification accuracy in run 3 was 90.09%, besides, the lowest number, 87.85% was belonging to run 2. Similarly, the highest and lowest growth of accuracy have been observed in subject 2 in such way that they were equal to 90.70% and 88.55%, respectively. In the same token, in subject 3, the highest and lowest growth of accuracy were equal to 97.20% and 94.02%. Just as, in subject 4, they were equal to 93.81% and 89.89%. In the same way, in subject 6, the above parameters were obtained equal to 95.12% and 90.45%. For subject 7, they were equal to 95.73% and 92.19%. As same, for subject 8 and 9, the highest numbers were 94.66% and 91.31% and the lowest numbers were equal to 91.43% and 87.82% interval.
The mentioned parameters showed that the lowest superiority has been achieved in subject 9 and the highest superiority belonged to subject 3. Contrary to the proposed method, in the NHP, the evaluated average accuracy based on different runs was not stable. It can be clearly seen that in subject 6, overall runs the NHP method achieved some accuracy in the range of 81.29% to 89.39%. Whereas, based on the proposed, the trend of accuracy in the same subject was in the small range approximately 4.67% which demonstrate that it is a more reliable method. The maximum variance of the proposed method based on average accuracy has been obtained 3.54% in subject 7, on the other hand, the maximum number based on NHP was 9.8% in subject 7 which shows approximately three times of variance against our proposed method.
In a similar way, the minimum variance of the proposed scheme according to average accuracy has been obtained 4.67% in subject 6, whereas, the minimum of the NHP method was 8.1% which was belonging to subject 6 and shows approximately two times of variety against our proposed method.
A main drawback of the proposed method is to compute several derivatives, which likely need to be worked analytically in advance of iterating to a solution. Therefore, the proposed method has to handle computational complexity in large problems with many variables or constraints.
| Conclusion|| |
In this article, a new adaptation method for hyperparameters in CNNs was introduced to improve the detecting of P300 signal in BCI applications. The proposed method has been based on quadratic optimization of Adadelta error which has been illustrated in the form of a set of qualities and inequalities.
To figure out the efficiency of the proposed method, it was compared with the NHP, Adam, and RMSprop techniques in terms of TPR, TNR, FPR, FNR, and accuracy. The obtained results showed that by performing the proposed optimization, a better well-fit model had been obtained for distinguishing P300 and non-P300 signals. The average accuracy of the proposed method had been at least 7.03%, 5.73%, and 6.56% better than the best among of NHP, Adam, and RMSprop methods, respectively.
Moreover, the achieved TPR was also confirmed the superiority of the proposed method in such way that it was 14.05%, 9.71%, and 12.35% better than alternatives (i.e., NHP, Adam, and RMSprop). Similarly, FPR, FNR, and TNR of the proposed method showed considerable superiorities (1.37, 1.81, 1.54), (14.12, 9.78, 12.42), and (1.37, 1.81, 1.54) percent, respectively, against the alternative methods. Furthermore, it was understood that the performance of the proposed method has had lower sensitivity against different stimulating patterns (i.e., runs) than the best among alternatives based on accuracy. The accuracies which have been obtained from proposed method against several runs showed the variances approximately 2–3 times lower than the same variances which had been obtained for its alternative.
Based on the mentioned results, it may be concluded that the proposed method has a considerable potential to improve P300 detection in BCI applications.
Financial support and sponsorship
This research was supported in part by Iranian Research Organization for Science and Technology.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]
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