Neuromarketing is an emerging research field that aims to understand consumers’ decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.

Electrocardiogram (ECG) is a record of the heart’s electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq −7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system.

Photoplethysmogram (PPG) signal reacts according to the volumetric change of blood, that is related to the total cardiovascular system. Hence PPG signals have a very close relation with cardiovascular diseases. As the number of cardiovascular disease (CVD) patients is increasing day by day, it is necessary to develop digital systems for early detection and treatment. In this study, we have aimed to design a field programmable gate array (FPGA) based hardware system that can detect various CVDs. We have chosen six different combinations of diseases from the PPG BP dataset. The selected signals are denoised using filters, and then features are extracted. Then SVM classifier is used to determine the class of the preprocessed signals. For FPGA implementation, we have targeted Xilinx zynq ultrascale+. The system utilizes different logic and signal processing blocks to detect the result. The resource utilization analysis proves the efficacy of the selected FPGA board. The power utilization analysis depicts that the designed prototype requires 1.403 W of power, which is sensible due to the requirement of large number of logic blocks. The accuracy of the system of detecting multiclass diseases is 79.83%. In the future, the system can be further extended to detect other types of CVDs.

Software-defined networks (SDNs) are the future networks that enable the system to be more flexible and programmable using a centralized controller. Field-programmable gate arrays (FPGAs) serve as exemplary hardware to implement these adaptable networks. Ternary content-addressable memory (TCAM) is an essential part of every network to perform packet classification and forwarding, but they are missing in modern FPGAs. Researchers and FPGA vendors have proposed several designs to emulate TCAM using available memories on FPGA, but they are power inefficient due to the activating of entire circuitry in a single search operation. In this brief, we propose a novel power-aware reconfigurable FPGA-based TCAM architecture that enables only a portion of the hardware to perform the search operation. We performed an extensive design space exploration to find the optimal number of banks on Xilinx FPGAs, which provides the maximum power saving. Moreover, we propose a solution to bank overflow using backup CAM (BUC) to handle the overflowed CAM entries. The proposed TCAM improves the power consumption by 40% and maintains one-clock cycle update latency with no compromise on the throughput of the system compared with the state-of-the-art FPGA-based TCAM architectures.

Electroencephalogram (EEG) is the graphical representation of Brain’s electrical activity. Mental stress can be detected in many ways and EEG is one of them. Regular mental stress gives rise to many mental disorders and it may cause various physiological and psychological diseases. As a result, early-stage detection of stress is very important. In this research, brain activity was recorded through EEG headset during inducing different levels of stress from audio-visual stimulus. Again, for better interaction between humans and machines, it is essential to analyze the power spectrum of the brain in response to different audio and visual stimulus. To better evaluate visual and auditory stress, an automated system is designed to differentiate among various audio and visual evoked potentials. This may further help for designing different assistive devices for the people having visual and hearing disability. In this paper, we proposed a framework to classify different levels of stress in response to audio and visual stimuli and also classified between these two stimuli by analyzing EEG signals. Raw EEG data was collected in lab environment and the necessary pre-processing steps were applied for denoising. By extracting robust features from the denoised audio and visual data, binary and multi-level stress were classified. A binary classification between audio and visual stimuli was also successfully done in this research. We achieved highest accuracy for binary stress classification 97.14% from visual stimuli, whereas we achieved 94.51% accuracy for auditory stimuli. Again, we achieved the accuracy for four level stress classification 89.59% for visual stimuli and 82.63% for audio stimuli.