# Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

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## Abstract

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## 1. Introduction

- Based on the traditional genetic algorithm, the specific immunity operator is introduced to improve the accuracy and accelerate the convergence of the algorithm, and the results are further optimized with a forbidden search algorithm;
- The adaptive threshold setting method is proposed to allow the threshold set by the binarization network to be dynamically altered in accordance with the different brain network features to increase the accuracy of the outcomes of brain division;
- Q and NMI metrics derived by the techniques are reliable and superior to the currently used brain network division algorithms under the same experimental settings;
- The algorithm is used to divide the brain network into communities in various emotional states and investigate changes in how the brain communicates across regions and interaction patterns when processing emotional activity, yielding results that are largely consistent with previous physiological findings.

## 2. Method

#### 2.1. Algorithm Framework

#### 2.2. Data Pre-Processing

#### 2.2.1. EEG Emotional Database (DEAP)

#### 2.2.2. Pre-Processing Methods

#### 2.3. Network Feature Extraction

#### 2.3.1. PLV Statistical Analysis

#### 2.3.2. Matrix Binarization

#### 2.4. Connectivity Analysis

#### 2.4.1. Brain Function Network Establishment

#### 2.4.2. Brain Network Structure Division

#### 2.5. Evaluation Function

#### 2.5.1. Normalized Mutual Information

#### 2.5.2. Modularity

## 3. Results and Discussion

#### 3.1. Threshold Determination

#### 3.2. Division of Brain Functional Networks in Different Emotional States

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Brain network community division flow. The algorithm steps are shown in (

**a**–

**e**): (

**a**) Acquisition and pre-processing of EEG data. (

**b**) Calculate phase-locked values to construct the complex network. (

**c**) Set the threshold to generate the binarization matrix. (

**d**) Build the functional brain network based on binarization matrix element values. (

**e**) MFMICD algorithm-based functional brain network division.

**Figure 5.**The relationship between NMI values and the number of unprivileged network edges: (

**a**) S2; (

**b**) S5.

**Figure 6.**Performance comparison of MFMICD algorithm with Ncut and GAcut algorithms: (

**a**) the evaluation function is Q; (

**b**) the evaluation function is NMI.

**Figure 7.**PLV-based fully linked matrix of S2: (

**a**) positive status; (

**b**) negative status. Different colors represent different weights. The correlation between nodes is inversely correlated with the square’s color: the redder the square, the higher the connection; the bluer the square, the lower the correlation.

**Figure 9.**Structural diagram of modular brain network for S2.: (

**a**) positive status; (

**b**) negative status.

**Table 1.**Proportion of nodes belonging to each brain region in the top three modules of the S2 brain network to the total number of nodes in each brain region.

Nodes in Each Brain Region | Percentage | ||||
---|---|---|---|---|---|

Positive State | Negative State | Positive State | Negative State | ||

Frontal Lobe | Fp1 Fp2 F3 F4 Fz AF3 AF4 | Fp1 AF3 F3 AF4 Fz F4 | Fp1 AF3 F3 AF4 Fz F4 | 85.70% | 85.70% |

Temporal Lobe | F7 T7 P7 FC5 CP5 F8 T8 P8 FC6 CP6 | CP6 T7 FC6 FC5 | CP6 T7 FC6 F8 FC5 | 40% | 50% |

Parietal Lobe | P3 P4 Pz | None | None | 0% | 0% |

Occipital Lobe | O1 O2 Oz PO3 PO4 | Oz PO4 | Oz PO4 O2 | 40% | 60% |

Central Region | C3 C4 Cz CP1 CP2 FC1 FC2 | C4 C3 CP1 CP2 | C4 FC2 C3 CP1 CP2 | 57.10% | 71.40% |

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**MDPI and ACS Style**

Zhao, R.; Zhang, T.; Zhou, S.; Huang, L. Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm. *Brain Sci.* **2022**, *12*, 1159.
https://doi.org/10.3390/brainsci12091159

**AMA Style**

Zhao R, Zhang T, Zhou S, Huang L. Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm. *Brain Sciences*. 2022; 12(9):1159.
https://doi.org/10.3390/brainsci12091159

**Chicago/Turabian Style**

Zhao, Renjie, Tao Zhang, Shichao Zhou, and Liya Huang. 2022. "Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm" *Brain Sciences* 12, no. 9: 1159.
https://doi.org/10.3390/brainsci12091159