基于條件經(jīng)驗(yàn)?zāi)J椒纸夂痛⑿蠧NN的腦電信號(hào)識(shí)別
doi: 10.11999/JEIT190124
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重慶郵電大學(xué)自動(dòng)化學(xué)院 重慶 400065
Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition
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School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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摘要:
針對(duì)運(yùn)動(dòng)想象腦電信號(hào)(EEG)的非線性、非平穩(wěn)特點(diǎn),該文提出一種結(jié)合條件經(jīng)驗(yàn)?zāi)J椒纸?CEMD)和串并行卷積神經(jīng)網(wǎng)絡(luò)(SPCNN)的腦電信號(hào)識(shí)別方法。在CEMD過程中,采用各階固有模式分量(IMF)與原始信號(hào)的相關(guān)性系數(shù)作為第1個(gè)IMF篩選條件,在此基礎(chǔ)上,提出各階IMF之間的相對(duì)能量占有率作為第2個(gè)IMF篩選條件。此外,為了考慮腦電信號(hào)各個(gè)通道之間的特征和突出每個(gè)通道內(nèi)的特征,該文提出SPCNN網(wǎng)絡(luò)模型對(duì)進(jìn)行CEMD過程后的腦電信號(hào)進(jìn)行分類。實(shí)驗(yàn)結(jié)果表明,在自行采集的腦電數(shù)據(jù)集上平均識(shí)別率達(dá)到94.58%。在公開數(shù)據(jù)集BCI competition IV 2b上平均識(shí)別率達(dá)到82.13%,比卷積神經(jīng)網(wǎng)絡(luò)提高了3.85%。最后,在自行設(shè)計(jì)的智能輪椅腦電控制平臺(tái)上進(jìn)行了輪椅前進(jìn)、左轉(zhuǎn)和右轉(zhuǎn)在線控制實(shí)驗(yàn),驗(yàn)證了該文算法對(duì)腦電信號(hào)識(shí)別的有效性。
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關(guān)鍵詞:
- 腦電信號(hào)識(shí)別 /
- 經(jīng)驗(yàn)?zāi)J椒纸?/a> /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 特征提取 /
- 智能輪椅
Abstract:For the non-linear and non-stationary characteristics of motor imagery ElectroEncephaloGram (EEG) signals, an EEG signal recognition method based on Conditional Empirical Mode Decomposition (CEMD) and Serial Parallel Convolutional Neural Network (SPCNN) is proposed. In the CEMD process, the correlation coefficient between the Intrinsic Mode Functions (IMFs) and the original signal is used as the first condition to select IMFs. Based on this, the relative energy occupancy rates between the IMFs are proposed as the second condition to select IMFs. Further, to consider the characteristics between the EEG signal channels and highlight the features in each EEG signal channel, a SPCNN model is proposed to classify the processed EEG signals. The experimental results show that the average recognition rate reaches 94.58% on the dataset collected by ourselves. And the average recognition rate reaches 82.13% on the BCI competition IV 2b dataset, which is 3.85% higher than the average recognition rate of convolutional neural network. Finally, the online control experiments are carried out on the designed intelligent wheelchair platform, which proves the effectiveness of the proposed algorithm for EEG signals recognition.
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表 1 不同算法對(duì)5受試者腦電信號(hào)的識(shí)別準(zhǔn)確率(%)
算法 CSP ACSP DBN CNN STFT-CNN SPCNN 本文CEMD-SPCNN S01 65.00 77.50 87.08 86.25 88.75 90.42 93.33 S02 81.67 82.92 87.50 87.92 89.17 91.25 94.17 S03 98.33 97.08 95.83 95.83 96.67 97.08 99.16 S04 76.25 78.33 83.33 85.42 85.42 86.25 89.58 S05 95.42 96.25 93.75 91.67 92.50 94.17 96.67 均值 83.33 86.41 89.50 89.42 90.50 91.83 94.58 方差 190.01 91.88 26.51 18.61 18.18 16.62 13.02 下載: 導(dǎo)出CSV
表 2 不同算法對(duì)BCI competition IV 2b數(shù)據(jù)集的識(shí)別準(zhǔn)確率(%)
算法 Chin Gan Coyle CSP ACSP DBN CNN STFT-CNN SPCNN 本文CEMD-SPCNN B01 70.00 71.00 60.00 66.56 67.50 66.56 72.22 75.00 76.39 80.56 B02 61.00 61.00 56.00 57.81 55.31 62.50 61.03 61.76 63.24 64.71 B03 61.00 57.00 56.00 61.25 62.19 60.00 61.11 62.50 62.50 64.58 B04 98.00 97.00 89.00 94.06 94.69 96.87 98.65 98.65 99.32 99.32 B05 93.00 86.00 79.00 80.63 76.88 82.19 86.48 87.16 87.84 88.51 B06 81.00 81.00 75.00 75.00 75.94 77.50 79.17 80.56 81.25 83.33 B07 78.00 81.00 69.00 72.50 71.25 76.56 78.47 77.08 79.17 81.25 B08 93.00 92.00 93.00 89.38 89.38 88.75 86.18 86.18 86.84 90.13 B09 87.00 89.00 73.00 85.63 81.25 85.94 81.25 82.64 84.03 86.81 均值 80.22 79.44 72.22 75.86 74.93 77.43 78.28 79.06 80.06 82.13 方差 192.19 190.03 181.69 158.75 157.50 155.85 147.92 138.63 137.52 129.78 下載: 導(dǎo)出CSV
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