Muhammad Ibn Ibrahimy

Electrical cell-substrate impedance sensing (ECIS) based biosensor for characterization of DF-1 cells

Electric cell-substrate impedance sensing (ECIS) method can be used as a valuable tool for real time monitoring of cell behavior such as attachment, mobility, and growth. Changes in impedance of the cells due to growth and attachment can be modeled as an equivalent circuit consisting of resistors and capacitors of both the cell culture media and the cells. In this work, a biosensor which measures the impedance in DF-1 cells (derived from chicken embroyonic fibroblasts and CEF cells) are presented. The biosensor consists of a Teflon cell holder and two gold electrodes. Experimental measurements were conducted using DF-1 cells cultured in DMEM media. Two different experiments were conducted namely; the control experiment (holder contains only DMEM media) and the cell experiment (holder contains both DMEM media and cells). The biosensor was placed in an incubator with optimum settings for cell growth. Impedance measurements were sampled at six hour intervals. Based on these measurements the resistance and capacitance change due to the growth of the DF-1 cells were calculated. It was observed that significant change in resistance and capacitance values occurs in the first six hours, where cell growth and attachment is most active. After this period of time, the cells become confluent and capacitance values become saturated.

Classification of Multichannel EEG Signal by Linear Discriminant Analysis

Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Linear Discriminant Analysis (LDA) has a very low computational requirement which makes it suitable for online BCI system. This paper proposes an advanced and simple classification technique for MI related BCI system. Initially the signal is extracted for different features. The LDA classifier has been used to propose technique to design an MI based BCI. For contrastive comparison other classification techniques have been evaluated by classification accuracy and Cohen’s kappa.