dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Elodie Boller ,  Paul Tafforeau , and  Alexander Rack  for providing advice on the design of the tomography setup, and  Sanchez Technology  for building the deformation apparatus. The  Norwegian Research Council  (grant  300435  to JAM) and  U. S. Department of Energy  (award  DE-SC0016520  to YBZ) funded this work. Beamtime was allocated at the  European Synchrotron Radiation Facility (Long Term Proposal   ES-295 ). The experimental data (time series of X-ray tomograms) are available on Norstore: including the three monzonite samples (Renard, 2017(Renard, , 2018)), the two Carrara marble samples (Renard, 2019) and the three Westerly granite samples (Renard,  2021 ). The code used to build the machine learning models are available on Github (https://github. com/jmcbeck/Machine_Learning_Rock_Deformation).
The model proposed in this paper contains more parameters (10 against 4) but ensures the stress continuity. Due to the variety of the considered situations, we have developed in Section 5. 1 specific identification strategies based on: 1) the linear or nonlinear nature of the material parameters of the model; 2) the modeling of the free boundary conditions by a spectral approach. These strategies have been implemented in the Octave free software  (Eaton et al. , 2016).
(  65 ) with respect to lp: M nlp ð Þlp ¼ e nlp ð Þ ð66Þ Note that Eq. (  66 ) is a linear system with respect to lp, but not with respect to nlp. We therefore need to solve iteratively Eq. (  66 ) inside a loop. We have implemented this loop in the Octave free software  (Eaton et al. , 2016) , and the optimization related to the nonlinear material parameters was performed with the fminbnd or fminsearch functions, depending on if the number of nonlinear parameters is one or strictly greater that one. The corresponding algorithm is presented in Fig. 5. It should be noted that splitting the material parameters into two categories allows to save computation time because the optimization related to the nonlinear material parameters concerns a small number of parameters and solving the linear system (66) with respect to the linear material parameters is very fast.
Moreover, it is proved in section 5. 4 that performing an identification process based on shear experiments only require 7 of the 10 material parameters of our model. However, in the most complicated loading case mixing tensile and shear tests, the use of the 10 material parameters embedded in our model is required. In order to identify the 10 material parameters of the proposed model, we have developed and implemented specific original optimization tools inside the Octave free software because we have to cope with a wide variety of loading cases: uniaxial tension and shear tests, both parallel and transverse to the fiber direction  (Ciarletta et al. , 2011; Davis and De Vita, 2014) , as well as shear tests with 9 different fiber angles  (Horgan and Murphy, 2017). The non-standard identification strategies we have implemented are based on: 1) the linear or nonlinear nature of the material parameters; 2) the modeling of the free boundary conditions by a spectral approach.
Acknowledgements This work was supported by the  National Key R&D Program of China  [grant number  2017YFB0703200 ], the  National Natural Science Foundation of China  [grant number  11772274 ],  Guangdong New Energy Vehicle Power and Safety System Engineering Technology Research Center , the  Science and Technology Planning Projects of Guangzhou, China  [grant number  201803030041 ] and the  Science and Technology Planning Projects of Guangzhou, China  [grant number 201905010007].
REMERCIEMENTS This thesis is a result of a research work, whereby I have been accompanied and fostered by many. It is a pleasant moment that now I have the opportunity to express my gratitude to all of them. Firstly I thank God for His blessing that I am finally able to complete my research and thesis. Secondly, I would like to thank my supervisor, Dr Nasreen badruddin. She has always been a good advisor, provided proper guidance and delivered valuable feedback on the progress of my research, despite her busy schedule. Her consistent support, advices, views, comments and discussion have led to completion of this thesis successfully. I would also like to sincerely appreciate my co-supervisors, Dr. Vijanth S Asirvadam, Dr. Eric Fauvet, Dr. Christophe Stolz and my field supervisor, Dr. Tahamina Begum.
1. 6/ SCOPE OF STUDY This research explores a suitable algorithm to identify eyeblink artifacts that are contaminating the EEG signals without any manual supervision, with an automated varying threshold value. The eyeblink artifacts are detected to assist in the subsequent artifact removal process. For this purpose, the algorithm is evaluated on MATLAB using an EEG dataset collected from Hitachi. The decomposition and blind source separation techniques, specifically the EMD and CCA techniques, are modified and investigated for their ability to remove the identified eyeblink artifacts effectively, with the aim to help online removal of eyeblink artifacts feasible. The proposed algorithms to remove eyeblink artifacts from EEG signals are designed from the modified EMD and CCA. The investigation and evaluation of the proposed algorithms are conducted on the EEG dataset of Hitachi.
Apart from that, a publicly available EEG dataset that contains involuntary eyeblinks were used to evaluate the consistency of performance of the proposed algorithms. All investigation and evaluation are performed offline in MATLAB. The proposed algorithms are then developed and investigated in the C++ programming language. The primary purpose is to investigate the computation time or the processing speed the proposed algorithms, FastEMD-CCA 2 and FastCCA could achieve, in line to support an online application, while being able to retain the neural information on the artifact-free EEG segments. The Hitachi EEG dataset is used for this investigation.
2. 2/ RELATED WORK ON ARTIFACT DETECTION Many methods were developed in the past to automate identification or detection of eyeblink artifacts in the EEG signal. One of the easiest and preferred way for eyeblink artifact recognition is by merely using an amplitude threshold  [23]. This method determines if an eyeblink artifact is present in an EEG segment if the amplitude in the segment exceeds a predetermined amplitude threshold. In most EEG-based applications, this method is preferred due to its straightforwardness. However, eyeblink artifact detection through an amplitude threshold may not be suitable in applications that require high detection accuracy  [24]. This method can also be useful when there is an abundance of EEG data available, thus discarding required EEG segments or retaining unwanted EEG segments may not cause any critical issue to the EEG signal analysis  [25].
As elaborated earlier for the amplitude threshold, applying a fixed threshold value for the features discussed may lead to detection errors due to individual variance in blinking pattern and blinking strength. As a result, the threshold values may need to be tailored for every individual, making the technique impractical in online applications. Recently, WD Chang et al. [29]  have proposed a method that combines digital filters and a rule-based decision system. This method may not be useful for applications that require online processing of EEG data because it depends on the proper selection of threshold value for every EEG dataset. The same authors have proposed an automatic approach in  [30]  so that their existing method could support real-time eyeblink artifact detection. They have combined the digital filters with an automatic thresholding algorithm proposed by Kim et al.
2. 5/ SUMMARY Contamination from eyeblink artifacts in EEG signals is inevitable, which could pose an impact on the desired EEG application. To this extent, many studies have been conducted and various methods are developed to identify and remove eyeblink artifacts from EEG signals. However, studies and work to deal with online removal of eyeblink artifacts remain an attractive research area. Every online artifact removal technique discussed in this chapter depends on either a dedicated artifact reference recording or some kind of training data that records artifacts separately for training purposes, which may add some time delay to the techniques in online applications. There are still many issues in making artifact removal algorithms truly real-time, reliable and practical. It has to be concluded that studies addressing real-time processing and removal of eyeblink artifacts from EEG signals are crucial.
2. Synthetic eyeblink artifacts are added to the EEG dataset to produce contaminated EEG signal. The synthetic eyeblink artifacts, eb(t) can be simulated through exponential functions with different amplitudes, as in Eq. (3. 53). eb(t) = 40e -(10t-10) 2 + 40e -(10t-30) 2 + 32e -(10t-45) 2 + 28e -(10t-70) 2 (3. 53) Contaminated EEG signal can be obtained by adding the EEG dataset s(t) with synthetic eyeblink artifacts, as in Eq. (3. 54). X(t) = eb(t) + s(t) (3. 54) 3. The second multidimensional data set, Y(t) is taken, with Y(t) = X(t -1).
6. Canonical variates U are computed by projecting the estimated de-mixing weight matrix A onto the mean removed signal X as in Eq. (3. 26). The resulting canonical variate U, is shown in Fig. 3. 6. Clean EEG signal that is free from artifacts can be reconstructed by taking the inverse of de-mixing matrix, A -1 , into the non-artifactual canonical components, U clean : The evaluation is carried out on clean EEG signals of 5 randomly selected subjects, each with 5 seconds of length. These clean EEG signals are added with synthetically created eyeblink artifacts from Eq. (3. 53) to obtain 5 seconds length of contaminated EEG signals. X clean = A -1 U clean (3. Contaminated EEG datasets are subjected to artifact elimination through CCA, with three matrix decomposition techniques discussed in subsections 3.
The higher oscillations in the raw EEG signal will be isolated out in the first few IMFs, while the sum of remaining IMFs would by default produce an eyeblink artifact trend. Partially reconstructing the highly oscillating IMFs which are lower in amplitude would yield the EEG trend. Alternatively, low oscillating IMFs with high amplitudes are summed together to obtain the eyeblink artifact trend. Manual observation on IMFs produced by 30 EEG signals from the Hitachi dataset reveals that the clean EEG components are most often sifted out in the first two IMFs, while the remaining IMFs and the residual signal comprise the eyeblink artifact components. Therefore, the FastEMD algorithm is designed to decompose the raw EEG signal to up to 5 IMFs only, which is sufficient to segregate out the clean EEG signal and the eyeblink artifact trend.
The accuracy represents the ability of the proposed algorithms in accurately identifying the eyeblink artifacts and uncontaminated EEG segments, in order for it to remove the eyeblink artifacts. The proposed algorithms have achieved an average of 97. 9% and 99. 47% of accuracy compared to 91. 7% by FORCe on the Hitachi dataset. While for the INV-SK dataset, FastEMD-CCA 2 's average accuracy is 97. 8%, FastCCA's is 98. 3% and FORCe's is 96. 6%. The proposed algorithms achieved a slightly higher accuracy level compared to the FORCe algorithm. Failing to correctly identify the eyeblink artifacts and EEG segments are interpreted through the error rate produced by the algorithms, where it counts the chance of the algorithms to miss an eyeblink artifact. FastEMD-CCA 2 produced an average error rate of 2.
10%, FastCCA with 0. 53%, while FORCe yield 8. 30% on the Hitachi dataset. For the INV-SK dataset, the average error rate of FastEMD-CCA 2 , FastCCA and FORCe is 2. 19%, 1. 67% and 3. 40% respectively. This denotes that these algorithms are still susceptible to miss out eyeblink artifacts, regardless of the EEG dataset. FORCe records the highest error rate and lowest accuracy level on average among the three algorithms for both datasets. The average eyeblink artifact detection and removal accuracy of FastCCA is 1. 57% higher and the average error rate of FastCCA is 1. 57% lower compared to FastEMD-CCA 2 with Hitachi's dataset. Similarly, with INV-SK's dataset, FastCCA's average accuracy is 0. 52% higher than FastEMD-CCA 2. This shows that FastEMD-CCA 2 has a higher probability of identifying eyeblink artifacts or EEG segments erroneously.
Apart from error rate and accuracy, sensitivity is used as a measurement to measure how precise the algorithms are in correctly identifying and removing the eyeblink artifacts in comparison with the actual number of observed eyeblink artifacts. Similarly, specificity is used to measure the precision of the algorithms in properly recognizing EEG segments and retaining them. Again FORCe records the lowest sensitivity on average for both datasets, but not significantly low. The results indicate that the proposed algorithms, FastCCA and FastEMD-CCA 2 have achieved 99. 44% and 97. 65% of sensitivity respectively, which are 9. 97% and 8. 18% higher than that of FORCe algorithm for Hitachi's dataset. Evaluating the INV-SK dataset also reveals that FORCe is 2. 28% and 1. 83% lower in sensitivity percentage than FastCCA and FastEMD-CCA 2 respectively.
74%, 99. 22% and 98. 65% respectively on the Hitachi dataset. Specificity of 100% is achieved by all three algorithms on the INV-SK dataset. This signifies that these algorithms do not introduce much distortion to the neural information of the EEG signals under evaluation. From the comparison, it is clear that FastCCA and FastEMD-CCA 2 have achieved better performance than FORCe on the same set of EEG signals. The average computation time FastCCA and FastEMD-CCA 2 took to remove eyeblink artifacts from 14-channel EEG signals with an average signal length of 312s (Hitachi dataset) is 6. 87 and 6. 78 seconds respectively, while FORCe took 85. 10 seconds. FastCCA, FastEMD-CCA 2 and FORCe took 7. 20s, 4. 31s and 85. 94s respectively on the other dataset, INV-SK, with an average signal length of 266s.
The computation time of FastEMD-CCA 2 and FastCCA is at least 12 times faster than that of FORCe for Hitachi's dataset. On INV-SK's dataset, FastEMD-CCA 2 is 19 times faster and FastCCA is 12 times faster than FORCe. It has to be noted that although FastCCA is better than FastEMD-CCA 2 as demonstrated by accuracy, error rate and sensitivity measurement, it is slower than FastEMD-CCA 2 in terms of processing time. This can be due to the difference in the detection mechanism of the eyeblink artifact events in these algorithms. In FastEMD-CCA 2 , eADA is used to locate a suitable eyeblink artifact frame, which is then extracted out as a general eyeblink artifact template using FastEMD with CCA. The extracted general eyeblink artifact template is used to identify the remaining eyeblink artifact events in that EEG signal.
However, the results are not significantly better than FastEMD-CCA 2. Considering the average processing time, FastEMD-CCA 2 is a better choice for online applications compared to FastCCA, but again the processing time of FastEMD-CCA 2 is not significantly better than FastCCA. So, both proposed algorithms can be used for applications requiring online removal of eyeblink artifacts with high accuracy. Despite the fact that the FastEMD-CCA 2 and FastCCA algorithms achieve attractive performance scores on both Hitachi and INV-SK datasets, these algorithms still fail to detect and remove some of the eyeblink artifacts. FastEMD-CCA 2 was unable to detect and remove nearly 137 and 103 eyeblink artifact events on the Hitachi and INV-SK's dataset respectively, which accounts for about 2% of error rate.
Subsequent application of CCA to this EEG segment to remove eyeblink artifact is irrelevant and may cause loss of neural information. Whereas in FastCCA, CCA is directly applied to EEG segments identified contaminated by eyeblink artifacts with the existing eADA algorithm. This allows an adaptive detection and removal of eyeblink artifacts for every event of blink. Thus the algorithm is not dependent on a general template for artifact detection. This explains why the average accuracy, sensitivity and specificity of FastEMD-CCA 2 are lower than FastCCA by 1. 57%, 1. 79%, and 0. 52%, respectively on the Hitachi dataset. In summary, it can be concluded that in FastEMD-CCA 2 , eyeblink artifacts that are irregular in pattern compared with the eyeblink template extracted are not classified as eyeblink artifacts, thereby not getting removed.
5. 2. 3. 1/ WELCH ANOVA RESULTS FOR HITACHI DATASET The computation time, accuracy, sensitivity, specificity and error rate for FORCe and the proposed algorithms, FastEMD-CCA 2 and FastCCA for the Hitachi dataset are checked if statistically significant. Again, before conducting the ANOVA test, the normality of these parameters is tested with the Shapiro-Wilk. The distribution normality of these parameters is given in Table  5. 9. From the Welch ANOVA test, a significant difference for the time is observed between FastEMD-CCA 2 , FastCCA and FORCe. Also between FastEMD-CCA 2 and FastCCA the P-value is 0. 992, which means the computation time between FastEMD-CCA 2 and FastCCA is not statistically significant. Statistical significance observed on error rate, accuracy and sensitivity between all three algorithms, FORCe, FastEMD-CCA 2 and FastCCA too, with P=0.
5. 2. 3. 2/ WELCH ANOVA RESULTS FOR INV-SK DATASET Similar to the Hitachi dataset, the Welch ANOVA is carried out on the INV-SK dataset. The test results are provided in Table  5. 11. the need to have an EOG channel as an artifact reference. Analysis proved that the proposed algorithms, FastEMD-CCA 2 and FastCCA are computationally fast and accurate. The algorithms appear promising to be used for online applications. The computation environment is crucial for any application that requires online processing. Currently, MAT-LAB is the most popular tool used for research purposes, but for online artifact removal applications, MATLAB alone may not be a feasible platform. Hence, the next chapter will discuss the implementation of eADA, FastEMD and CCA in an inexpensive computing environment, C++, to investigate the feasibility of the algorithms to support online applications.
[128]EG dataset that is used for evaluation in this thesis were collected at Hitachi, Hatayoma site in Japan. EEG signals from volunteers were obtained according to the regulations of the internal review board on Central Research Laboratory, Hitachi, Ltd. , following receipt of written informed consent. The approval number is 20131021-0138. These EEG signals have been primarily collected to conduct a study on mental stress. Since all recorded signals were contaminated by eyeblink artifacts, the dataset is appropriate for use in this research. These EEG signals are recorded using 14 free electrodes placed on the scalp following the 10-20 system. The EEG signals were collected from 10 participants with 6 recordings from each participant, resulting in 60 EEG signals. The participants are aged between 30 and 55 years.
All recorded signals are of different durations, which were recorded at a sampling rate of 256 Hz. This EEG dataset is mainly used to verify various techniques and algorithms discussed and proposed throughout the thesis. The total number of eyeblink artifacts found in this dataset are more than 5600, which were identified through manual inspection. The number of eyeblink artifacts contaminating each of the EEG signal varies, ranging from 20 to 172 occurrences. Apart from the Hitachi dataset collected, a publicly available EEG dataset which contains involuntary eyeblinks were used, which was collected through an experiment by Kanoga et al. [128]. These EEG signals are recorded using 14 electrodes, following the 10-20 system. This dataset consists of EEG signals from 20 participants, with 3 recordings from each participant, resulting in 60 EEG signals.
The participation in the study was approved by the Research Ethics Committee of Keio University, Japan. All recorded signals are of different durations, which were recorded at a sampling rate of 256 Hz. This EEG dataset is used as an additional dataset for validation purpose. The total number of eyeblink artifacts found in this dataset are more than 4600, which were identified through manual inspection. The number of eyeblink artifacts contaminating each of the EEG signal varies, ranging from 5 to 200 occurrences.
FORCe algorithm. The outcomes are then averaged for each of the dataset. In MATLAB 2018b, the EEG recordings are imported into the workspace and processed automatically to remove the eyeblink artifacts in windows, with each window being at least 1-second in length for FastEMD-CCA 2 , FastCCA and FORCe. The FastEMD-CCA 2 algorithm operates as discussed in sections 4. 2, 4. 3. 1 and 4. 3. 2, while the FastCCA algorithm works as discussed in section 4. 4. The FORCe algorithm first applies wavelet decomposition on each channel of an EEG signal. The resulting approximation coefficients obtained through wavelet are subjected to ICA to get independent components, ICs. Next, the artifactual ICs are identified through several threshold criteria, where ICs exceeding certain threshold values are classified as eyeblink and cardiac artifacts respectively, and thus removed.
e. FORCe using two EEG datasets. Tese algorithms have outperformed FORCe in terms of artifact removal accuracy and processing speed, making them a feasible solution for applications requiring online removal of eyeblink artifacts. Additionally, FastCCA and FastEMD-CCA 2 algorithms are developed and implemented in C++ programming language and compared with implementation in MATLAB. This is done to analyse the processing speed that can be achieved by the proposed algorithms in C++. The performance of the proposed algorithms in removing eyeblink artifacts online without distorting the neural signal is evaluated and quantitatively analysed. The artifact removal accuracy of FastCCA is slightly better than FastEMD-CCA 2 in both MATLAB and C++, indicating FastCCA is preferable. It appears that both algorithms have achieved instantaneous eyeblink artifact elimination, with FastEMD-CCA 2 slightly faster than FastCCA.
The participation in the study was approved by the Research Ethics Committee of Keio University, Japan. All recorded signals are of different durations, which were recorded at a sampling rate of 256 Hz. This EEG dataset is used as an additional dataset for validation purpose. The total number of eyeblink artifacts found in this dataset are more than 4600, which were identified through manual inspection. The number of eyeblink artifacts contaminating each of the EEG signal varies, ranging from 5 to 200 occurrences. 4. 1. 2. 2/ PUBLIC DATASET (INV-SK) 4. 1. 2/ REAL SIGNALS 4. 1. 2. 1/ HITACHI DATASET 4. 2/ PROPOSED EYEBLINK ARTIFACT DETECTION ALGORITHM (EADA) 4. 2. 1/ DETERMINATION OF EEG SEGMENTS WITH EYEBLINK ARTIFACTS.
4. 5. 1/ EYEBLINK ARTIFACT DETECTION 4. 5. 1. 1/ EADA VS FIXED THRESHOLDS Sixty EEG signals from the Hitachi EEG dataset, from section 4. 1. 2. 1 are used in this analysis. Eyeblink artifacts can be clearly captured in the frontal channels, Fp1-Fp2 elec- trodes of the EEG recordings. Hence, the proposed algorithm is evaluated on the frontal channel, Fp1, of these EEG signals. For comparison purpose, eADA is compared with the conventional constant threshold method. The constant threshold method is performed by fixing the threshold values, with different amplitude displacement, for example, thresholds of more than 10uV, 20uV, 30uV, 40uV and 50uV. Whichever amplitude displacement that exceeds these thresholds are considered to be eyeblink artifacts. The performance of the proposed approach, eADA, is measured by validating if it is ac- curate in identifying eyeblink artifacts in comparison with the use of a fixed threshold value.