P300-based Brain-Computer Interface Performance Enhancement

P300-based Brain-Computer Interface Performance Enhancement Enhancing Performance and Bitrates in a P300-based Brain-Computer Interface for Disabled Subjects by Phase-to-Amplitude Cross-Frequency Coupling Stavros I.Dimitriadis1,2*, Avraam D. Marimpis3 1Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK 2Cardiff University Brain Research Imaging Center, School of Psychology, Cardiff University, Cardiff, UK 3Brain Innovation B.V., Netherlands Abstract A brain-computer interface (BCI) is a communication system that transforms brain-activity into specific commands for managing a computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain-activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach of a well-known P300-BCI system for disabled subjects. Estimating cross-frequency coupling (CFC) and namely ÃŽÂ ´-ÃŽÂ ¸ phase-to-amplitude coupling (PAC) within sensor, we succeeded high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is tested with four severely disabled and four able-bodied subjects. The bitrates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 10 bits/sec. The new preprocessing approach based o n recordings from the single-sensor Pz while the classification accuracy is tested also for others electrodes. Keywords: Brain-computer interface; P300; Disabled subjects; cross-frequency coupling; accuracy *Corresponding author: Dr.Dimitriadis Stavros Research Fellow School of Medicine, Cardiff University, UK CUBRIC Neuroimaging Center, Cardiff,UK Introduction From the very first work of Farwell and Donchin [1] the majority of P300-based Brain Computer Interface (BCI) systems focused on developing new application scenarios [2,3], and on developing and testing new algorithms for the reliable detection of the P300 waveform from noisy datasets [4-8]. For a review of P300, an interested can read the [9-10]. Ten years ago, two pioneer studies have been first published presenting a P300 BCI system on disabled subjects. Piccione et al. (2006) [11] design a 2D cursor BCI control system where subjects had to concentrate on four arrows flashing every 2.5 sec in random order occupied the peripheral area of a computer screen. Five disabled and seven able-bodied subjects were participated on this experiment. For controlling this cursor, this four-choice P300 flashing arrow cursor was used. EEG signals were recorded using four EEG sensors and electro-oculogram. Using independent component analysis and neural networks, Piccione et al. [11] demonstrated that P300 can be a valuable control signal for disabled subjects. However, the communication system was too low compared to state of the art systems [5,8]. Sellers and Donchin (2006) [12] designed a four choice BCI experiment with four stimuli (YES, NO, PASS, END) that were presented every 1.4 s in random order, in two different modalities either visual or auditory or in a combined mode. Three subjects suffering with ALS and three able-bodied subjects performed the experiment. EEG recordings from three sensors were classified using a stepwise linear discriminant algorithm (LDA). They demonstrated that communication via a P300 system is possible for subjects suffering from ALS. Additionally, they demonstrated that communication is possible in different modalities like the visual, auditory, and also in a combined auditory-visual mode. However, both the classification accuracy and the communication rate were low compared to the state of the art results. One of possible explanations of low accuracy and communication rate could be the low number of EEG sensors, the long inter-stimulus intervals and the low number of trials. McCane et al., demonstrated a BCI system where both accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, the target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN) [13]. Hoffmann et al.,, demonstrated a six-choice P300 paradigm which was tested in a population of five disabled and four able-bodied subjects. Six different images were flashed in random order with an ISI of 400 ms [7]. They tested how the electrode configuration can influence the accuracy in order to detect the best channel selection. For four out of five disabled subjects and for all the able-bodied subjects both the communication rates and the classification accuracies were higher compared to the aforementioned studies [11,12]. The datasets in Hoffmann et al., study can be freely downloaded from the website of the EPFL BCI group (http://bci.epfl.ch/p300). In the present study, we used the dataset from Hoffmann et al., study to demonstrate an alternative algorithmic approach with main scope to improve the bitrates up to the limits. For that occasion, we adopted a cross-frequency coupling (CFC) estimator namely phase-to-amplitude coupling (PAC) to quantify how the phase of the lower frequency brain rhythms modulates the amplitude of the higher oscillations. The whole approach was followed in a trial basis and within sensors located over parieto-occipital brain areas. PAC proved to be a valuable estimator in many applications like the design of a biomarker: for amnestic mild cognitive impairment subjects during an auditory oddball paradigm [14], for dyslexia [15], for mild traumatic brain injury [16]. The layout of the paper is as follows. In Section 2, we described the subject population, the experiments that were performed, and the methods used for data pre-processing steps of the proposed pipeline and the classification procedure. Results are presented in Section 3. Discussion is devoted in Section 4. 2. Materials and Methods 2.1. Experimental setup Users were facing a laptop screen on which six images were displayed (see Fig. 1). The images showed a television, a telephone, a lamp, a door, a window and a radio. The images were selected according to an application scenario in which users can control electrical appliances via a BCI system. The application scenario served however only as an example and was not pursued in further detail. The images were flashed in random sequences, one image at a time. Each flash of an image lasted for 100 ms and during the following 300 ms none of the images was flashed, i.e. the inter-stimulus-interval was 400 ms. The EEG was recorded at 2048 Hz sampling rate from 32 electrodes placed at the standard positions of the 10-20 international system. A Biosemi Active Two amplifier was used for amplification and analog to digital conversion of the EEG signals. [Figure 1 around here] 2.2. Subjects The proposed methodology was applied to P300 BCI-oriented recordings derived from five disabled and four healthy subjects. The demographics of the four disabled are presented in (Table 1). Disabled subject 5 as excluded from further analysis. Subjects 6-9 were Ph.D. students that were recruited from EPFL BCI groups laboratory (all males, age 30 ±2.3). None of subjects 6-9 had known neurological deficits. For more information regarding the subjects, an interested reader should refer to the original paper [7]. Table 1. Subjects from which data was recorded in the study of the environment control system S1 S2 S3 S4 Diagnosis Cerebral palsy Multiple sclerosis Late-stage amyotrophic lateral sclerosis Traumatic brain and spinal-cord injury, C4 level Age 56 51 47 33 Age at illness onset 0 (perinatal) 37 39 27 sSex M M M F Speech production Mild dysarthria Mild dysarthria Severe dysarthria Mild dysarthria Limb muscle control Weak Weak Very Weak Weak Respiration control Normal Normal Weak Normal Voluntary eye movement Normal Mild nystagmus Normal Normal 2.3. Experimental schedule Each subject completed four recording sessions. The first two sessions were performed on one day and the last two sessions on another day. All subjects were recruited within maximum two weeks between the first and the last session. Each of the sessions consisted of six runs, one run for each of the six images. For further details about the protocol followed on this experiment see the original paper related to this dataset [7]. The following protocol was used in each of the runs. (i) Subjects were asked to count silently how often a prescribed image was flashed (for example: Now please count how often the image with the television is flashed). (ii) The six images were displayed on the screen and a warning tone was issued. (iii) Four seconds after the warning tone, a random sequence of flashes was started and the EEG was recorded. The sequence of flashes was block-randomized, this means that after six flashes each image was flashed once, after twelve flashes each image was flashed twice, etc. The number of blocks was chosen randomly between 20 and 25. On average 22.5 blocks of six flashes were displayed in one run, i.e. one run consisted on average of 22.5 target (P300) trials and 22.5ÃÆ'-5 = 112.5 non-target (non-P300) trials. (iv) In the second, third, and fourth session the target image was inferred from the EEG with a simple classifier. At the end of each run the image inferred by the classification algorithm was flashed five times to give feedback to the user. (v) After each run subjects were asked what their counting result was. This was done in order to monitor performance of the subjects. The duration of one run was approximately one minute and the duration of one session including setup of electrodes and short breaks between runs was approximately 30 min. One session comprised on average 810 trials, and the whole data for one subject consisted on average of 3240 trials. 2.4 Offline Analysis The impact of different single-sensor recordings on classification accuracy was tested in an offline procedure. For each subject four-fold cross-validation was used to estimate average classification accuracy. More specifically, the data from three recording sessions were used to train a classifier and the data from the left-out session was used for validation. This procedure was repeated four times so each session served once for validation. 2.4.1. Preprocessing Before learning a classification function and before validation, several preprocessing operations were applied to the data. The preprocessing operations were applied in the order stated below. Referencing. The average signal from the two mastoid electrodes was used for referencing. Filtering. A third order forward-backward Butterworth bandpass filter was used to filter the data.. The MATLAB function butter was used to compute the filter coefficients and the function filtfilt was used for filtering. The predefined frequencies were : ÃŽÂ ´ {0.5-4 Hz},ÃŽÂ ¸ {4-8 Hz},ÃŽÂ ±1 {8-10 Hz}, ÃŽÂ ±2 {10-13 Hz},ÃŽÂ ²1 {13-20 Hz},ÃŽÂ ²2 {20-30 Hz} and ÃŽÂ ³1 {30 45 Hz}. (iii) Downsampling. The EEG was down-sampled from 2048 Hz to 512 Hz by selecting each 4th sample from the bandpass-filtered data. (iv) Single trial extraction. Single trials of duration 1000 ms were extracted from the data. Single trials started at stimulus onset, i.e. at the beginning of the intensification of an image, and ended 1000 ms after stimulus onset. Due to the ISI of 400 ms, the last 600 ms of each trial were overlapping with the first 600 ms of the following trial. (v) Electrode selection. We applied our analysis to recordings from single-sensor activity and mainly, PZ,OZ,P3,P4,P7 and P8. (vi) Feature vector construction. As appropriate feature for each trial, we used the phase-to-amplitude coupling (PAC) which already has been shown its potentiality in building reliable biomarkers (Dimitriadis et al., 2015,2016). PAC was estimated for each frequency pair (see ii)). The description of PAC is given in the next section. As a complementary feature that can separate the counted stimuli from the non counted stimuli, alpha relative signal powers have been estimated. Alpha power level can give us a valuable and objective criterion when a subject attends or not attends to the stimulus. Our idea is to create an initial binary classifier that will cut-off the attended from the non-attended stimuli for each subject prior entering the main multi-class classifier. CFC metric computation CFC estimates the strength of pairwise interactions and identifies the prominent interacting pair of frequencies, both between and within sensors [17-19]. Among available CFC descriptors, phase-amplitude coupling (PAC), which relies on phase coherence, is the one most commonly encountered in research [20]. The PAC algorithm as adapted to continuous MEG multichannel recordings is described below. ÃŽÂ ¤he within-sensor CFC version is described. Let x(isensor, t), be the EEG activity recorder at the isensor-th site, and t=1, 2,. T the successive time points. Given a frequency-limited signals x(isensor,t) , cross-frequency coupling is estimated by allowing the phase of the lower frequency (LF) oscillations to modulate the amplitude of the higher frequency (HF) oscillations. The complex analytic representations of each signal zLF(t) and zHF(t) are derived via the Hilbert transform (HT[.]). Next, the envelope of the higher-frequency oscillations AHF(t) is bandpass-filtered within the range of LF oscillations and the resulting signal is submitted to an additional Hilbert transform to derive its phase dynamics component à Ã¢â‚¬  '(t) which expresses the modulation of the amplitude of HF-oscillations by the phase of the LF-oscillations. Phase consistency between the two time-series was measured by means of both the original definition [21] and the imaginary portion of PLV, as synchronization indexes to quantify the strength of PAC. The original PLV is defined as follows: and the imaginary part of PLV as follows: The imaginary portion of PLV is considered to be less susceptible to volume conduction effects in assessing CFC interactions. While the imaginary part of PLV is not affected by volume conduction effects, it could be sensitive to changes in the angle between two signals, which not necessarily imply a PLV change. In general, the imaginary portion of PLV is only sensitive to non-zero-phase lags and is thus resistant to instantaneous self-interactions associated with volume conductance [22]. For further details and applications, an interested reader can read our previous work [14,15]. In the present study, as was already mentioned we used 8 frequency bands which means that PAC is estimated for 7*6/2=21 cross-frequency pairs e.g. ÃŽÂ ´Ãƒ Ã¢â‚¬   ÃŽÂ ¸A ,ÃŽÂ ´Ãƒ Ã¢â‚¬   ÃŽÂ ±1Awhere à Ã¢â‚¬   and A denote the phase and amplitude of each frequency band. Figure 2 demonstrates the pre-processing steps of the PAC estimator for a trial of subject 6 at target image 6. [Figure 2 around here] Signal Power We estimated the relative power of each band-pass frequency signal segment with the following equations: The first equation quantifies the signal power (SP) of each frequency as the sum of the filtered signal squared per sample (3) while equation (4) divides the SP by the sum of the SP from all the frequencies which gives the relative signal power (RSP). The whole approach was repeated for every trial, sessions and subject. 2.4.4. Machine learning and classification Training data sets contained 405 target trials and 2025 non-target trials and validation data sets consisted of 135 target and 675 non-target trials (these are average values cf. Section 2.3). Adopting sequential feature selection algorithm, we detected the characteristic cross-frequency pair via PAC value that gives the highest discrimination of each target images compared to the rest based on the training data set. Additionally, we used the same feature extraction algorithm to detect the relative signal power that separate the counted flashing images from the non-counted images. We trained a multi-class SVM classifier based on the selected PAC estimate from specific cross-frequency pairs and then we tested the classifier to the validation data to get the response tailored to each target image [23]. The training test consisted of the first session while the rest three sessions were used for validating the whole analytic scheme. A k-nearest neighbour (k-NN) classifier was applied to differentiate the attended from the non-attended flashing images prior to multi-class SVM classifier. 2.4.5 Performance Evaluation Classification accuracy and ITR were calculated for the offline experiments separately. The method for calculating ITR (in bits per second) was as follows (5): Where N is the number of classes (i.e., 6 in this study), P is the accuracy of target identification, and T (seconds per selection) is the average time for a selection. Results ÃŽÂ ´-ÃŽÂ ¸ Phase-to-Amplitude Coupling as a Valuable Feature for the BCI-P300 System We estimated both PAC and relative signal power (RSP) for the first 32 samples (60 ms) increasing the window up to 500 ms (256 samples) with a step of 12 samples (5 ms) . The sequential feature selection algorithm detected only one PAC feature from the 21 possible cross-frequency pairs as the unique candidate feature to separate the six classes of images-stimuli. ÃŽÂ ´Ãƒ Ã¢â‚¬   ÃŽÂ ¸A was the selected feature for both disabled and able-bodied subjects. The group-averaged classification performance was for each sensor location using the first 100 ms for both able-bodied and disabled subjects. The errors were detected on the trials where the subject missed the flashing image. The classification performance with the use of a kNN-classifier prior to the multi-class SVM was 100 % for every subject and for all the pre-selected sensors namely PZ,OZ,P3,P4,P7,P8 EEG sensors. Figure 3 and Figure 4 illustrates the trial-related (grand-averaged) PAC-connectivity patterns (comodulograms) for subject 6 (able-bodied) and subject 1 (disabled) correspondingly from target and non-target trials for each flashing image. Comodulograms differed by contrasting target vs non-target within each subject and target image but also between the two images. ÃŽÂ ´Ãƒ Ã¢â‚¬   ÃŽÂ ¸A was the unique feature for both disabled and able-bodied subjects that can clearly predict the target image for both groups. [Figures 3 and 4 around here] Attention and Alpha Power Prior to multi-class SVM, we applied a kNN-classifier based on ÃŽÂ ±1 signal power which was selected as the feature that can discriminate counted from non-counted flashing images. The kNN-classifier performed 100 % clear filtration of attended from non-attended trials for each subject and further improved the performance of multi-class SVM to 100 %. We achieved this performance using ÃŽÂ ±1 signal relative power estimated from the first 100 ms for both able-bodied and disabled subjects. The classification performance with the kNN-classifier was 100 % for every subject and for all the pre-selected sensors namely PZ,OZ,P3,P4,P7,P8 EEG sensors. Table 2 summarizes the group-averaged relative signal power (RSP) of ÃŽÂ ±1 frequency band for attended versus non-attended images. Table 2. Group-averaged ÃŽÂ ±1 signal relative power for attended and non-attended images. Attended Non-Attended Able Bodied Disabled Performance Evaluation In the present study, we succeeded bitrates of 10 bits/sec for both disabled and able-bodied subjects correspondingly for all the sensor locations used on the whole analysis. The time for estimation of PAC and testing the trial was 0.00001 sec on a Windows 7 -Intel 7 8-core machine. Discussion A novel approach of how to analyse single-trials in a BCI system was introduced based on the estimation of cross-frequency coupling (CFC) and namely phase-to-amplitude coupling (PAC). PAC was estimated within EEG sensors from single-trials recorded during a visual evoked experimental paradigm. The proposed analytic scheme based on the extraction of unique features from the CFC patterns on a single-trial basis and namely the ÃŽÂ ´Ãƒ Ã¢â‚¬   ÃŽÂ ¸A coupling, as a unique feature for both able-bodied and disabled subjects. Our experimentations showed a high classification rate (99.7%) based on the proposed PAC feature. Additionally, the superiority of our approach compared to alternative popular methodologies like the use of the original recordings was evident from the succeeded bitrates (10 bits/sec) and also of the response time of the classification system (0.00001 sec). Complementary, using a binary classifier trained with ÃŽÂ ±1 relative signal power prior to the multi-cl ass SVM, we differentiated the attended from the non-attended stimuli which further improved the classification performance up to 100% in both groups. Compared to many other P300-based BCI systems designed for disabled users, we succeeded the highest classification accuracy and bitrates higher than the original presented dataset [7]. In previous studies like the one of Sellers and Donchin (2006) [12], the best classification accuracy for the able-bodied and ALS subjects was on average 85% and 72% correspondingly [12]. Hoffmann et al., succeeded absolute classification accuracy for both disabled and able bodied subjects for the first demonstration of the current dataset. However, he used longer time series of over 15-20 secs by concatenating trials in order to train better the classifier. Additionally, he used one classifier per image per each of the twenty block and the final outcome derived as the majority voting of the twenty classifiers. Here, using phase-to-amplitude coupling as the appropriate descriptor of the evoked response in the parietal brain areas and a multi-class SVM classifier, we performed almost absolute accuracy ( 99.97) on a trial basis. Using an additional binary k-NN classifier and ÃŽÂ ±1 relative signal power prior to multi-class SVM, we separated the attended (counted) from the non-attended (not counted) trials leading to zeroing the misclassified trials from the multi-class SVM classifier for every subject. This procedure further improved the classification performance from 99.97 to 100% for each subject. We succeeded bitrates faster (10 bits/sec) than any other BCI system including the fastest spelling system presented recently (5.32 bits/sec ; [24]). In a previous study of Piccione et al. (2006) [11] average bitrates of about 8 bits/min were reported for both disabled and able-bodied subjects. Hoffman et al., 2008 [7] reported average bitrates obtained with electrode configuration (II) (8 electrodes) 12.5 bits/min for the disabled subjects and 10 bits/min for the able-bodied subjects. According to Klimeschs ÃŽÂ ± theory, on the early stages of perception, ÃŽÂ ± directs the information flow towards to neural substrates that represent information relevant for the encoding system (e.g. visual stimulus to visual system, voice/sound to auditory system). The physiological main function of ÃŽÂ ± is linked to inhibition. Klimeschs ÃŽÂ ± theory hypothesizes that ÃŽÂ ± enables to have access to stored information by inhibiting task-irrelevant neuronal substrates and by timing/synchronizing the cortical activity in task-relevant neuronal systems. A lot of research findings showed that both evoked ÃŽÂ ± and phase locking are evidence of a successful encoding of global stimulus features in an early post-stimulus interval of about 0-150à ¢Ã¢â€š ¬Ã¢â‚¬ °ms [25]. Besides the cross low-frequency/high-frequency coupling (e.g.,ÃŽÂ ¸-ÃŽÂ ³; 26,27), there are many evidences [28-31] that CFC exists also between the low-frequency bands (e.g., delta-theta, delta-alpha, and theta-alpha). Lakatos et al. (2005) [29] introduced a hypothesis about the hierarchical organization of EEG oscillations suggesting that the amplitude of the brain oscillations at a characteristic frequency band can be modulated by the oscillatory phase at lower frequency. In particular, they found that ÃŽÂ ´ (1-4 Hz) phase modulates ÃŽÂ ¸ (4-10 Hz) amplitude, and ÃŽÂ ¸ modulates ÃŽÂ ³ (30-50 Hz) amplitude in primary auditory cortex of awake macaque monkeys [29]. This multiplex coupling or nesting of brain rhythms might reflect a general brain organizational principle, as evidence of coupling (mainly ÃŽÂ ¸-ÃŽÂ ³) has also been observed in animals (e.g. rats,cats) and humans [32]. For instance, in auditory cortex, ÃŽÂ ´-band modulates the amplitude of ÃŽÂ ¸-band ICMs, whose phase in turn modulates the amplitude of ÃŽÂ ³-band ICMs [33]. This indirect enhancement effect uses the ongoing activity of local neural activity in the primary auditory cortex. Their hypothesis supports the notion that neural oscillations reflect rhythmic shifting of excitability states of neural substrates between high and low levels. This hypothesis is supported by the fact that oscillations can be predicted by visual input such as the auditory input arrives during a high excitability phase and is amplified. In the present study, we demonstrated that ÃŽÂ ´ (0.5-4 Hz) phase modulates ÃŽÂ ¸ (4-8 Hz) amplitude over visual brain areas due to flashing images, their content and mainly was observed on parietal EEG recording sites. We should also mention that the reason why ÃŽÂ ´Ãƒ Ã¢â‚¬   ÃŽÂ ¸A coupling discriminates the six flashing images can be directly linked to the content of the images. Visual attention samples image stimuli rhythmically demonstrating a peak of phase at 2 Hz [34] while flashing images induced rhythmic fluctuation at higher frequencies (6-10 Hz) [35] here within ÃŽÂ ¸ frequency range [4-8 Hz].Finally, the work of Karakas et al., ([36]) showed that the ERP represents interplay between the oscillations that are mainly in the ÃŽÂ ´ and ÃŽÂ ¸ frequencies and directly linked to P300 [37]. Conclussion In this work, an efficient algorithmic approach was presented to a P300-based BCI system for disabled subjects. We have shown that absolute classification accuracies and the highest reported bitrates can be obtained for severely disabled subjects under the notion of cross-frequency coupling and namely phase-to-amplitude coupling. Specifically, ÃŽÂ ´ (0.5-4 Hz) phase modulates ÃŽÂ ¸ (4-8 Hz) amplitude proved to be the candidate feature from PAC estimates that supported the highest classification accuracy, the fast bitrates and the fast response time of the multi-class system. Due to the use of the P300, only a small amount of training (trials from 1st session as a training set and 100ms per trial) was required to achieve good classification accuracy. Future improvements to the work presented could be the design of useful BCI applications adapted to the needs of disabled users. Also it might be useful to perform exploratory analysis on larger populations and on real-time to further validate the results found in the present work. Acknowledgements SID was supported by MRC grant MR/K004360/1 (Behavioural and Neurophysiological Effects of Schizophrenia Risk Genes: A Multi-locus, Pathway Based Approach) References Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988;70:510-23. Polikoff J, Bunnell H, Borkowski W. Toward a P300-based computer interface. In: Proceedings of the RESNA95 Annual Conference; 1995. Bayliss JD. Use of the evoked P3 component for control in a virtual apartment. IEEE Trans Neural Syst Rehab Eng 2003;11(2):113-6. Xu N, Gao X, Hong B, Miao X, Gao S, Yang F. BCI competition 2003 Data Set IIb: Enhancing P300 wave detection using ICA-based subspace projections for BCI applications. IEEE Trans B

Envy and Beauty in Snow White Essay examples -- essays research papers

Envy, Beauty, and Snow White Few people can grow up within today's society without knowing the tale of Snow White. From the Grimm Brothers to Disney, it has been told and retold to children throughout the ages. However, what is often overlooked are the true meanings within the story. Fairytales typically have underlying messages that can be found written between the lines, generally in terms of the key themes. Snow White discusses the themes of envy and beauty, and shows how humans' obsessions can lead to their own downfall as well as the harm of others. When focusing on the relationship between Snow White and her step-mother the Queen, it is evident that the combination of these two themes results in a power struggle in which beauty is seen as a commodity and is the basis for the step-mother?s envy towards Snow White. From the very beginning of the tale it is obvious that the Queen is obsessed with beauty, ?the King took another wife, a beautiful woman, but proud and overbearing, and she could not bear to be surpassed in beauty by anyone(Grimm and Grimm 166). Further evidence of her narcissism is her daily ritual in which she consults her magic mirror on who is the most beautiful person in the kingdom. As she repeatedly expects the answer to be in her favor, she is outraged when it appears that Snow White has surpassed her. This information drives the Queen to hate Snow White and soon she orders her death. By looking at beauty as a commodity through which power can be gained, this action can be interpreted as a means for the Queen to preserve her power through beauty. The fact that Snow White was beautiful may not have been reason enough alone to kill her, but the fear that Snow White could use her beauty in orde... ...intertwined as main themes within the story. The Queen?s fixation with her own beauty, and then her envy over Snow White?s, was the main component in causing her death. As she attempted numerous times to murder Snow White, she was in effect writing her own death sentence, because her obsession drove her beyond rational thinking to the point where her triumph over Snow White was more important than her own life. Works Cited: Grimm, Jacob, Wilhelm Grimm, Edgar Lucas, Lucy Crane, Marian Edwardes, and Fritz Kredel. Grimms' Fairy Tales. Illustrated junior library. New York: Grosset & Dunlap, 1945. Haase, Donald, ed. The Reception of Grimms' Fairy Tales : Responses, Reactions, Revisions. Detroit : Wayne State University Press, 1993. Sale, Roger. Fairy Tales and After: From Snow white to E. B. White. Harvard University Press, 1979.

Causes Of Climate Change

Climate change is when the climate changes by warming up or cooling down over a period of time. Climate change is occurring now, the climate is warming up dramatically, and this is known as global warming. In the past the temperature has gone up and has gone down, and the climate has warmed up and then after a period of time cooled down. The first way that climate changes naturally is by the amount of energy produced by the sun and how much we receive. This varies over time, sometimes more energy is produced by the sun and sometimes less is produced.When the sun produces more energy the climate warms up and when the sun produces less energy the climate cools down, this is because when the sun provides more energy the earth receives more heat, and warms up the climate and when the sun produces less energy the earth receives less heat so the climate cools down, this all happens over a long period of time. The second way the climate changes naturally is by how the earths orbit around th e sun changes. The earths orbit can change from orbiting the sun in a spherical way to an elliptical way.When the earth orbits the sun in an elliptical way the earth is often warmer, because at some points to earth comes closer to the sun in its orbit, and when the earth gets closer to the sun the energy has got less distance to travel, and when the sun and the earth are close together the suns energy has a smaller area to spread over so the energy warms up the climate in that part. The third way that climate changes naturally is by the change in the earth’s axis. At the moment the tilt of the earth is at 23.5 degrees.When the earth tilts away the earth gets less energy, and makes the climate cooler, but when the earth tilts the other way the earth gets more energy and warms up the climate. This takes place over 41,000 years. The fourth way that the climate changes naturally is as the earth wobbles as it spins on its axis, as the earth wobbles, one side tilts closer to the su n than the other, so the side that gets closer to the sun, receives more energy from the sun, and the  energy only has a small distance to travel so it heats up that part of the earth and warms up the climate, and the other side of the earth that wobbles away from the sun has less energy received by the sun and the energy has more distance to travel, so the side that wobbles away from the sun gets cooler and then the climate cools down, as some of it gets lost in space.The fifth way that the climate changes naturally is by all of the Co2 in the air, all of the carbon dioxide in the atmosphere is trapping all of the heat, produced by the sun, and this causes temperatures to rise.Records show that temperatures rose from 8000 BC dramatically even though there were few people on the earth, the earths population was around the population of London. This shows that the climate can warm up with very few people on the earth producing Co2 In conclusion people don’t always produce al l the Co2 in the atmosphere or cause the climate to warm up; it could sometimes just be our earth itself.

Health Promotion Plan For A New Framework Health Care...

Y.L.’ Health Promotion Plan Yanin C Lopez Excelsior College Introduction Jarvis (2012) noted the purpose of Healthy People 2020 and pointed out the determinants of health and health disparities in the creation of a new framework health care delivery that maintain and promote healthy communities. An Individuals’ health status is influenced by constellation of personal, social economic and environmental factors, collectively kwon as the Determinants of Health. Social factors such as poverty, occupational status, the quality of the neighborhood and environment; lifestyle factors and individual behaviors; biology and genetics together with the model of health care services are the points need to be evaluated and suggested to change and†¦show more content†¦Her health promotion plan will have active family and patient involvement because of the importance of the family in the clients ‘life. Y.L.’ Health Promotion Plan Identification of Issues: Y.L. Health history Y.L. is a female, Hispanic 44 year’s old patient who also speaks and read English, but it is her second language. She and her ancestors were born in Cuba; she was raised in Cuba and immigrated to the United States as an adult, and she is closely associated with her ethnic group of origin, but she had melt well with the American values and beliefs. She is a reliable source of information and knowledgeable; she is a Register Nurse. Y.L. has medical insurance, and she didn t have advance directives and never had thought about it, but she is willing to discuss and prepare it. Y.L. reached the primary physician because she is feeling fatigued, headache, moist cough and shortness of breath; she is using the rescue inhaler more frequently and feels without energy despite her efforts and the use over the counter medication to relief cold like symptoms, in the last two weeks. This client sees health as a wellbeing of mind and body, but she barely perform activities to promote and maint ain health, she devotes herself to the nursing profession and her family with little and none time for her to practice stress reduction interventions to improve and promote the well-being of her body and mind. Y.L. believes that illness is the