Evaluation of recording factors in EEG-based personal identification: A vital step in real implementations

Su, Fei; Xia, Liwen; Cai, Anni; Ma, Junshui

doi:10.1109/icsmc.2010.5641768

Cited by 16 publications

(15 citation statements)

References 15 publications

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“…More complex problems often require a larger number of anchor points to better model the data. Referring to the num ber of sub-segments, including more diverse EEG samples is a way to obtain better identification performance [5]. From Fig.2(a) and (b), we can conclude that longer recording dura tion achieves higher accuracy.…”

Section: Performance Evaluationmentioning

confidence: 91%

“…The subjects were required to stay calm during recording. The data collection scheme is the same as that in [4,5,6]. Each original EEG segment lasts for around 5 minutes.…”

Section: Datasetsmentioning

confidence: 99%

“…Most existing studies [4,5,6] have tried to address this problem by seeking a distinctive and stable feature repre sentation of EEG signals and making nearest neighbor (NN) search for identity classification of EEG signals. With the feature set combining auto-regression (A R) model parameter [7] and power spectrum density (PSD) [8,9], the identification system achieved an average accuracy of 97.3% on a dataset of 40 subjects at the recording duration of 3 minutes [4].…”

Section: Introductionmentioning

confidence: 99%

“…This promising performance proved the statistical commonality existed in one's EEG signals. Further, the effects of person's diet, circadian and recording durations on the EEG signals have also been evaluated quantitatively by the well-designed personal identification experiments [5]. It suggested that in cluding more diverse EEG samples (different diet, times of day, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Local metric learning for EEG-based personal identification

Cai

Liu

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There has been an increasing attention on Electroencephalo graph (EEG) based personal identification over the last decade. Most existing methods address this problem by Euclidean metric based Nearest Neighbor (NN) search. How ever, under various recording conditions, simple Euclidean distance cannot model the similarity relations between EEG signals precisely. To overcome this drawback, a local met ric learning based on Large Margin Nearest Neighbor (L LMNN) for EEG based personal identification is proposed in this paper. For each EEG sample, a separate local met ric is learned, making the distance between intra-class EEG samples minimized and simultaneously those of inter-class EEG samples maximized. To balance the locality and com putational efficiency, the local metrics are approximated by weighted linear combinations of a small set of anchor sam ples. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art methods. It improves the identification ac curacy overall, especially at shorter EEG durations, which is important for improving the practicability of EEG-based personal identification system.

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Section: Performance Evaluationmentioning

confidence: 91%

“…The subjects were required to stay calm during recording. The data collection scheme is the same as that in [4,5,6]. Each original EEG segment lasts for around 5 minutes.…”

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Local metric learning for EEG-based personal identification

Cai

Liu

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Considering that the diet and personal circadian are crucial factors affecting the EEG signals compared to other factors, we addressed how much the diet and times of day affected a person's EEG recordings [34]. It has shown that recording factors have some effects on the system performance, and if more diversity samples were included in modeling, the higher accuracy would be obtained.…”

Section: Naive Bayes Classifiermentioning

confidence: 99%

A dual-biometric-modality identification system based on fingerprint and EEG

Liwen

Cai

2010

2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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This paper proposes a dual-biometric-modality personal identification system, which has both outstanding identification performance and effective anti-spoofing property. This dual-modality system represents the first effort to fuse the conventional fingerprint with a novel biometric modality--Electroencephalogram (EEG). The fusion of these two modalities is achieved at the matching score level. Experimental results show that the highest identification performance is obtained in the dual-modality system, when its performance is compared with those of the systems using these two modalities separately. Furthermore, it is demonstrated that EEG is an appealing complementary modality to enhance the anti-spoofing capability of conventional biometrics-based identification systems.

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