Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is “slower” (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone

Epidemiology and patterns of care for invasive breast carcinoma at a community hospital in Southern India

<p>Abstract</p> <p>Background</p> <p>Breast cancer incidence in India is on rise. We report epidemiological, clinical and survival patterns of breast cancer patients from community perspective.</p> <p>Methods</p> <p>All breast cancer patients treated at this hospital from July 2000 to July 2005 were included. All had cytological or histological confirmation of breast cancer. TNM guidelines for staging and Immunohistochemistry to assess the receptor status were used. Either lumpectomy with axillary lymph node dissection or Modified radical mastectomy (MRM) was done for operable breast cancer, followed by 6 cycles of adjuvant chemotherapy with FAC or CMF regimens to patients with pT >1 cm or lymph node positive or estrogen receptor negative and radiotherapy to patients after breast conservation surgery, pT size > 5 cm, 4 or more positive nodes and stage IIIB disease. Patients with positive Estrogen receptor or Progesterone receptor were advised Tamoxifene 20 mg per day for 3 years. Descriptive analysis was performed. Independent T test and Chi-square test were used. Overall survival time was computed by Kaplan – Meier method.</p> <p>Results</p> <p>Of 1488 cancer patients, 122 (8.2%) had breast cancer. Of 122 patients, 96.7% had invasive breast carcinoma and 3.3% had sarcoma. 94% came from the rural and semi urban areas. Premenopausal women were 27%. The median age was 50 years. Stage I-6.8%, II-45.8%, III-22%, IV-6.8%, Bilateral breast cancer – 2.5%. The mean pT size was 3.9 cm. ER and PR were positive in 31.6% and 28.1% respectively. MRM was done in 93.8%, while 6.3% patients underwent breast conservation surgery. The mean of the lymph nodes dissected were 3. CMF and FAC regimens were used in 48.8% and 51.2% of patients respectively. FAC group were younger than the CMF group (43.6 yr vs. 54 yrs, P = 0.000). Toxicities were more in FAC than CMF group, alopecia (100% vs. 26.2%), grade2 or more emesis (31.8% vs. 9.2%), grade2 or more fatigue (40.9% vs.19%), anemia (43.1% vs. 16.6%). Median Survival for the cohort was 50.8 months. ER positive patients had better median survival (P = 0.05).</p> <p>Conclusion</p> <p>MRM was the most frequent surgical option. CMF and FAC showed equivalent survival. FAC chemotherapy was more toxic than CMF. ER positive tumors have superior survival. Overall 3 year survival was 70 percent</p

Filter bank extensions for subject non-specific SSVEP based BCIs

Recently, filter bank analysis has been used in several detection methods to extract selective frequency features across multiple brain computer interface (BCI) modalities due to its effectiveness and simple structure. In this work, we propose filter bank technique as a standard preprocessing method for popular training free multi-channel steady-state visual evoked potential (SSVEP) detection methods to overcome subject-specific performance differences and a general improvement in detection accuracy. Our study validates the effectiveness of filter bank extensions by comparing performance differences of multichannel methods with their filter bank counterparts using a forty target SSVEP benchmark dataset collected across thirty five subjects. The results demonstrate that the proposed two stage (a filter bank stage followed by SSVEP detection) implementation of popular multichannel algorithms provide significant improvement in performance at short datalengths of < 2.75 s (p < 0.001) and can be viewed as a potential standard detection approach across all SSVEP identification problems

Multichannel EEG compression : wavelet-based image and volumetric coding approach

In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of “lossy plus residual coding,” consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately.Accepted versio

A two-dimensional approach for lossless EEG compression

In this paper, we study various lossless compression techniques for electroencephalograph (EEG) signals. We discuss a computationally simple pre-processing technique, where EEG signal is arranged in the form of a matrix (2-D) before compression. We discuss a two-stage coder to compress the EEG matrix, with a lossy coding layer (SPIHT) and residual coding layer (arithmetic coding). This coder is optimally tuned to utilize the source memory and the i.i.d. nature of the residual. We also investigate and compare EEG compression with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding. The compression algorithms are tested with University of Bonn database and Physiobank Motor/Mental Imagery database. 2-D based compression schemes yielded higher lossless compression compared to the standard vector-based compression, predictive and entropy coding schemes. The use of pre-processing technique resulted in 6% improvement, and the two-stage coder yielded a further improvement of 3% in compression performance.Accepted versio

Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain-computer interfaces

Objective. This study introduces and evaluates a novel target identification method, latent common source extraction (LCSE), that uses subject-specific training data for the enhancement of detection of steady-state visual evoked potential (SSVEP). Approach. LCSE seeks to construct a common latent representation of the SSVEP signal subspace that is stable across multiple trials of electroencephalographic (EEG) data. The spatial filter thus obtained improves the signal-to-noise ratio (SNR) of the SSVEP components by removing nuisance signals that are irrelevant to the generalized signal representation learnt from the given data. In this study a comparison of SSVEP identification performance between the proposed method, extended canonical correlation analysis (ExtCCA) and multiset canonical correlation analysis (MsetCCA) was conducted using SSVEP benchmark data of 40 targets recorded from 35 subjects to validate the effectiveness of the LCSE framework. Main results. The results indicate that the LCSE framework significantly outperforms the other two methods in terms of both classification accuracy and information transfer rates (ITRs). Significance. The significant improvement in the target identification performance demonstrates that the proposed LCSE method can be seen as a promising potential candidate for efficient SSVEP detection in brain-computer interface (BCI) systems

Constructing an exactly periodic subspace for enhancing SSVEP based BCI

•A novel approach that maps EEG data onto an exactly periodic subspace is proposed.•EPSD employs the periodic characteristics of the SSVEP response to enhance its SNR.•EPSD exhibits robust performance compared to the other commonly used spatial filters.•The study confirms that EPSD is promising detection algorithm for SSVEP based BCI.A novel exactly periodic spatial filtering (EPSD) approach, that provides a robust detection performance, is introduced and evaluated in this study. The proposed method exploits the temporal properties of the steady-state visual evoked potential (SSVEP) response to construct an orthogonal and exactly periodic mapping that enhances the signal to noise ratio (SNR) of the SSVEP embedded in the electroencephalogram (EEG) data. The subspace of interest is constructed via the elimination of the signals spaces that does not constitute the exact period of the target frequency. The EPSD is evaluated on a 35 subject benchmark dataset collected using a 40 target SSVEP BCI system. The results reveal that the proposed EPSD spatial filter significantly enhances the performance of target detection. Further statistical tests also confirm that the EPSD is a potential alternative to the existing SSVEP spatial filters for realizing an efficient BCI system

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface

Traditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost.In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates.Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results.The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions.The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI)