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Brain-computer interface (BCI) provides a new communication channel between human brain and computer. In order to eliminate uncorrelated channels to improve BCI performance and enhance user convenience with fewer channels, this paper proposes a new framework using binary adaptive differential evolution bat algorithm (BADEBA). The framework uses the important ideas of differential evolution algorithm and bat algorithm to select electroencephalograph (EEG) channels and intelligently optimizes the parameters of support vector machine (SVM). It combines wavelet packet transform (WPT) and common space pattern (CSP) to achieve the goal of using fewer channels to obtain the best classification accuracy. The proposed framework is evaluated with a common data set (DEAP). The results show that, compared with genetic algorithm (GA), binary particle swarm optimization (BPSO) and bat algorithm, the proposed BADEBA in this framework only uses eight channels to improve the classification accuracy by 13.63% in the valence dimension and seven channels to improve the classification accuracy by 15.22% in the arousal dimension. In addition, the spatial distribution of the best channels selected by this method is consistent with the existing knowledge of brain structure and neurophysiology, which shows the accuracy and validity of this method.

Emotion recognition as an emerging research direction has attracted increasing attention from different fields and is promising for various application domains, especially in the fields of medicine [

BCI is a communication system that transmits information between brain and external devices. One of the main challenges of BCI is its personal dependence. Even if the same experiment is replicated in the same environment, different brain regions will be activated. To solve this problem, more EEG channels need to be added to obtain more decision signals. However, the use of multiple channels can cause additional problems, such as computational complexity, noise, and redundant signals, which will reduce the performance of BCI. In addition, the use of a large number of channels requires a longer preparation time, which directly affects the convenience of BCI. Therefore, the need for performance and convenience can be balanced by choosing the minimum number of channels to obtain the highest or required accuracy. Based on DEAP database, Jianhai Z et al. [

Based on the aforementioned reasons, this paper proposes a BADEBA-SVM framework for channel selection, which maximizes classification accuracy and minimizes the number of channels. The framework applies mutation, crossover, and selection mechanisms of the binary adaptive differential evolution algorithm to the bat algorithm, so that mutation mechanism is introduced into the bat algorithm. This can increase the diversity of the bat population and enhance the global optimization ability of the framework by preventing individual population falling into local optimum. Combining with WPT and CSP, the best classification accuracy can be achieved by using fewer channels.

Bat algorithm is a new heuristic swarm intelligence algorithm [

In bat algorithm, a bat represents a feasible solution, while the prey of a bat represents the optimal solution. The position of Bat i at t time is

When searching locally, a bat is randomly selected from the bat population, and according to (

Differential evolution algorithm is an evolutionary algorithm based on the population difference. It was proposed by Rainer Storn and Kenneth Price in 1997 to solve Chebyshev polynomials [

Cross-operation can increase the diversity of the population. Interindividual cross-manipulation of the t-generation population and its variant intermediates is performed based on (

Selection operation is to determine whether individual

This section provides a detailed overview of the proposed framework (Figure

BADEBA-SVM framework for emotion recognition.

In this framework, a novel evolutionary method is used to evaluate the fitness function value by validation set and optimize the channel subset and support vector machine parameters to obtain the highest classification accuracy and the minimum number of channels. The first step is to decompose the EEG signal into wavelet packet in frequency domain and then reconstruct the signal in the (

It is found that the choice of C and

In order to solve the optimization problem of discrete space such as channel selection, the differential evolution algorithm must be binary coded and the new mutation operator must be designed by using logic operation instead of arithmetic operation. In this paper, the following operations are defined first.

Differential evolution algorithm has three control parameters: population size N, difference vector scaling factor F, and crossover probability CR. F and CR have significant influences on the performance of the algorithm. F is set to be large in the early stage and small in the late stage, and CR is set to be small in the early stage and large in the late stage. This can improve the performance of the algorithm. The modified equation of the scaling factor F is as follows:

Dynamic CR can make the algorithm converge to the appropriate position with a higher probability in the early stage of global search. In this study, the strategy of linearly decreasing weighted CR values is adopted, and the equation is as follows:

This paper proposes a binary adaptive differential evolution bat algorithm, which introduces the mutation, crossover, and selection mechanism of binary adaptive differential evolution algorithm into bat algorithm. Compared with the bat algorithm, the difference between the two algorithms is that, in each evolutionary process, the evolved bat position

according to Eq.(

DEAP is a data set for emotion analysis using EEG, physiological and video signals [

WPT includes the decomposition and reconstruction of wavelet packet coefficients. Wavelet packet decomposition has been extensively used in the field of signal processing. Compared with wavelet decomposition, it cannot only decompose the signal orthogonally in the whole frequency band, but also select the corresponding frequency band adaptively according to the characteristics of the signal, so that it matches the signal spectrum and has higher time-frequency resolution. Based on the multiresolution characteristic of WPT, the optimal component combination relation of EEG signal can be selected, and the signal in the useful information frequency range can be extracted and reconstructed. The decomposition algorithm for the coefficients is obtained by (

The reconstruction algorithm for the wavelet packet coefficients is deduced as

Research [

For swarm intelligence optimization algorithm, parameters determine the performance of the algorithm. In terms of parameter setting, we have carried out multivariate sensitivity tests. For example, the range of bat population size is set to from 10 to 100, incrementing by 5 each time; the range of pulse A_{0} is set to from 0.05 to 0.9, incrementing by 0.05 each time. By changing the parameters at the same time to observe its impact on the classification results, the following parameters are finally determined to achieve better classification results.

In BCI system, BADEBA was used to select electrodes. Equation (_{0} to 0.25, the maximum pulse rate r_{0} to 0.5, the pulse frequency range to

In BCI system, GA was used to select electrodes. Equation (

In BCI system, the binary particle swarm optimization algorithm [_{1} and c_{2} to 2, r_{1}, r_{2} and

Using binary adaptive differential evolution bat algorithm for channel selection, experiments were conducted on the two dimensions of valence and arousal. The results are shown in Figure

Channels selected based on valence (a) and arousal (b).

Figure

Channels selected, average accuracies, and standard deviations (%) by four optimal algorithms in valence.

Methods | # Channel | Channel location | Mean ± S.D. |
---|---|---|---|

Origin(Baseline) | 32 | All | 55.97±13.83 |

GA | 15 | Fp1,AF3,F3,Fz,F4,FC1,FC2,T7, | 61.63±12.54 |

BPSO | 12 | Fp1,Fp2,AF3,F3,F4,FC2,T7,CP1, | 69.38±10.76 |

BA | 10 | Fp1,Fp2,F3,F4,C4,T8,P3,O1,Oz,O2 | 72.42±9.93 |

BADEBA | 8 | Fp1,F3,F4,C4,CP1,P3,PO4,O1 | 75.26±9.72 |

As can be seen from Table

Channels selected, average accuracies, and standard deviations (%) by four optimal algorithms in arousal.

Methods | # Channel | Channel location | Mean ± S.D. |
---|---|---|---|

Origin(Baseline) | 32 | All | 55.24±14.21 |

GA | 14 | Fp1,Fp2,AF3,Fz,F4,T7,Cz,T8,CP2, | 60.76±12.86 |

BPSO | 11 | Fp1,AF3,Fz,F4,FC2,T7,CP2,CP6, | 69.95±11.38 |

BA | 9 | Fp2,AF3,F3,FC2,C3,T8,P3,PO3,O2 | 73.51±10.17 |

BADEBA | 7 | Fp2,AF3,T8,P3,P4,Oz,O2 | 75.98±10.85 |

According to the channels selected by the four algorithms, in order to find out which band of signals contributes most to classification results, we reconstruct the original signals in the

Classification results in

In order to verify the performance of the proposed framework, we use the test set to evaluate the performance of the proposed model. The classification accuracy of the model optimized by BADEBA algorithm is 74.86% and 75.61% in the two dimensions of valence and arousal, respectively. The results of DEAP data classification are compared with those of other literature in Table

Comparison with related work using EEG signals in DEAP dataset.

Authors | # Channel | Approach in the model | Accuracy(%) |
---|---|---|---|

Chung et al. [ | 32 | Power spectral analysis with Bayes classifier | 66.6(valence,2-class) |

32 | 53.4(valence,3-class) | ||

Yoon et al. [ | 32 | FFT enhanced feature extraction and classification | 70.9(valence,2-class) |

32 | 70.1(arousal,2-class) | ||

Dai et al. [ | 5 | Sparse constrained differential evolution enabled hybrid selection | 73.0(valence,2-class) |

5 | 74.5(arousal,2-class) | ||

Our research | 8 | BADEBA optimal channel and classification | 74.86(valence,2-class) |

7 | 75.61(arousal,2-class) |

As can be seen from Table

Based on the emotion recognition of EEG signals, a novel evolutionary optimization method is proposed to select the important channels and optimize the parameters of support vector machine to achieve higher classification accuracy. This paper has two main contributions. One is to combine the binary adaptive differential evolution algorithm with the bat algorithm to improve the diversity of the population, which not only has a strong global search ability in the early iteration, but also has a strong local search ability in the late iteration. The other is to improve the signal-to-noise ratio of emotion-related EEG signals through wavelet packet decomposition and reconstruction and then extract EEG features by CSP filtering for classification. The results show that the combination of signals in the (

The Deap dataset is a multimodal dataset for the analysis of human affective states. The electroencephalogram (EEG) and peripheral-physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. The data used to support the findings of this study were supplied by Queen Mary University of London, United Kingdom, under license and so cannot be made freely available. Requests for access to these data should be made to Queen Mary University of London, United Kingdom,

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This work was supported in part by the National Natural Science Foundation of China under Grant 61373116 and Grant 61572399 and the Project of Science and Technology Department of Shaanxi Province of China (Grant No. 2019ZDLGY07-08, 2018GY-013).