Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former connects the brain with a computer or external device while the later connects the nervous system to an implanted device. A BCI receives the modulated input from user either invasively or non-invasively. The modulated input, concealed in the huge amount of noise, contains distinct brain patterns based on the type of activity user is performing at that point in time. Primary task of a typical BCI is to find out those distinct brain patterns and translates them to meaningful communication command set. Cursor controllers, Spellers, Wheel Chair and robot Controllers are classic examples of BCI applications. This study aims to investigate an Electroencephalography (EEG) based non-invasive BCI in general and its interaction with a web interface in particular. Different aspects related to BCI are covered in this work including feedback techniques, BCI frameworks, commercial BCI hardware, and different BCI applications. BCI paradigm Steady State Visually Evoked Potentials (SSVEP) is being focused during this study. A hybrid solution is developed during this study, employing a general purpose BCI framework OpenViBE, which comprised of a low-level stimulus management and control module and a web based Google Street View client application. This study shows that a BCI can not only provide a way of communication for the impaired subjects but it can also be a multipurpose tool for a healthy person. During this study, it is being established that the major hurdles that hamper the performance of a BCI system are training protocols, BCI hardware and signal processing techniques. It is also observed that a controlled environment and expert assistance is required to operate a BCI system.

There are several commercial electroencephalography (EEG) devices on the market; however, affordable devices are not versatile for diverse research applications. The purpose of this project was to investigate how to develop a low-cost, portable, single-channel EEG system for a research institute that could be used for neurofeedback-related applications in homecare. A device comparison was intended to examine what system requirements such a system would need to achieve the secondary objective of developing a neurofeedback application that demonstrates the functionalities of the new device. A portable, single-channel EEG device prototype was realized that consisted of an amplifier module called EEG Click, a single-board microcontroller, an electrode cable, some disposable wet electrode pads, and a custom 3D-printed headband. Three pieces of software were developed: firmware for the prototype, two supporting computer applications for data recording, and visual neurofeedback. The neurofeedback application replayed a first-person view roller coaster video at a varying frame rate based on the theta band's mean power spectral density (PSD). The prototype was compared against a commercial device, InteraXon MUSE 2 (Muse). Technical measurements included determining the amplitude-frequency characteristics and signal quality, such as signal-to-noise ratio (SNR), spurious-free dynamic range (SFDR), and total harmonic distortion (THD). Furthermore, four physiological measurements were performed on six human test subjects, aged between 21-31 (mean: 26.0, std: 3.11), to compare the altered brain activity and induced artifacts between the two devices. The four tests were respiratory exercise, head movement exercise, eye movement exercise, and paced auditory serial addition test (PASAT), where each measurement included several epochs with various stimuli. After the recordings, PSD was calculated for each bandpass filtered epoch, then the spectra were split into theta (4-8 Hz), alpha (8-12 Hz), and beta bands (12-30 Hz). The PSD values were averaged within each frequency band, and then these baseline-corrected mean values were the input for the repeated measures ANOVA statistical analysis. Results revealed that the amplitude-frequency characteristic of the prototype was low-pass filter-like and had a smaller slope than Muse's. The prototype's SNR, including and excluding the first five harmonics, was 6 dB higher, while SFDR and THD for the first five harmonics were roughly the same as Muse's. The two devices were comparable in detecting changes in most physiological measurements. Some differences between the two devices were that Muse was able to detect changes in respiratory activity in the beta band (F(8,16) = 2.510, p = .056), while the prototype was more sensitive to eye movement, especially lateral and circular eye movement in theta (F(2,8) = 9.144, p = .009) and alpha (F(2,8) = 6.095, p = .025) bands. A low-cost, portable EEG prototype was successfully realized and validated. The prototype was capable of performing homecare neurofeedback in the theta band. The results indicated it is worth exploring further the capabilities of the prototype. Since the sample size was too small, more complex physiological measurements with more test subjects would be more conclusive. Nevertheless, the findings are promising; the prototype may become a product once. / Det finns flera kommersiella EEG-apparater (elektroencefalografi) på marknaden; däremot är de prismässigt överkomliga apparaterna inte mångsidiga nog för olika forskningsapplikationer. Syftet med detta projekt var att undersöka hur man kan utveckla en billigt, portabelt, enkanaligt EEG-system för ett forskningsinstitut som skulle kunna användas för neurofeedbackrelaterade tillämpningar inom hemsjukvård. En apparatjämförelse var tänkt att undersöka vilka systemkrav ett sådant system skulle behöva uppnå för att realisera det sekundära målet att utveckla en neurofeedback-applikation för att demonstrera den nya apparatens funktioner. En prototyp av en bärbar, enkanalig EEG-apparat skapades som bestod av en förstärkarmodul kallad EEG Click, en enkortsmikrokontroller, en elektrodkabel, några utbytbara våta elektrodkuddar och ett 3D-tryckt specialpannband. Tre mjukvarodelar utvecklades: en firmware för prototypen och två stödjande datorapplikationer, en för datainspelning och en för visuell neurofeedback. Applikationen för neurofeedback spelade upp en berg-och-dalbana för förstapersonsvisning med en varierande bildhastighet baserat på thetabandets effektspektrumet (eng. power spectral density, PSD). Prototypen jämfördes mot en kommersiell apparat, InteraXon MUSE 2 (Muse). Tekniska mätningar inkluderade fastställande av amplitud-frekvensegenskaper och signalkvalitet, såsom signal-brusförhållande (eng. signal-to-noise ratio, SNR), spuriosfritt dynamiskt område (eng. spurious free dynamic range, SFDR) och total harmonisk distorsion (eng. total harmonic distortion, THD). Vidare utfördes fyra fysiologiska mätningar på sex mänskliga deltagare (medelålder: 26,0, std: 3,11) för att jämföra de två apparaterna med avseende på mätningar av den förändrade hjärnaktiviteten och inducerade artefakter. De fyra testerna var andningsövningar, huvudrörelseövningar, ögonrörelseövningar, och paced auditory serial addition test (PASAT), där varje mätning innehöll flera epoker med olika stimuli. Efter inspelningarna beräknades PSD för varje bandpassfiltrerad epok, sedan delades spektrumet upp i theta-, alpha- och beta-band. Medelvärdet för PSD-värdena kalkylerades för varje frekvensband och dessa baseline-korrigerade medelvärden var indata till den beroende ANOVA statistisk analysen. Resultaten avslöjade att amplitud-frekvenskarakteristiken för prototypen var lågpassfilterliknande och hade en mindre lutning än Muses. Prototypens SNR, inklusive och exklusive de första fem harmonik, var 6 dB högre, medan SFDR och THD för de första fem övertonerna var ungefär desamma som Muses. De två apparaterna var jämförbara när det gäller att upptäcka förändringar i de flesta fysiologiska mätningar. Vissa skillnader mellan de två apparaterna var att Muse kunde upptäcka förändringar i andningsaktivitet i beta-bandet (F(8,16) = 2,510, p = 0,056), medan prototypen var mer känslig för ögonrörelser, särskilt de laterala och cirkulära ögonrörelser, i theta-bandet (F(2,8) = 9,144, p = 0,009) och alfa-bandet (F(2,8) = 6,095, p = 0,025). Prototypen var generellt mer känslig för grundläggande hjärnaktivitet, buller från omgivningen och artefakter. Sammanfattningvis konstruerades en billig, bärbar EEG-prototyp, vilketvaliderades framgångsrikt. Den anpassade enheten kunde utföra neurofeedback för hemsjukvård. Resultaten visade att det är värt att utforska prototypens möjligheter ytterligare. Eftersom stickprovet var relativt litet skulle mer komplexa fysiologiska mätningar med flera testpersoner krävas för att fastställa framtida användningsområden. Icke desto mindre är resultaten lovande; prototypen kan bli en produkt en gång.

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and to elucidate human neurophysiology. The advent of multi-channel micro-electrode arrays has driven the need for electronic store cord neural signals from many neurons. The continuous increase in demand of data from more number of neurons is challenging for the design of an efficient neural recording frontend(NRFE). Power consumption per channel and data rate minimization are two key problems which need to be addressed by next generation of neural recording systems. Area consumption per channel must be low for small implant size. Dynamic range in NRFE can vary with time due to change in electrode-neuron distance or background noise which demands adaptability. In this thesis, techniques to reduce power-per-channel and area-per-channel in a NRFE, via new circuits and architectures, are proposed. An area efficient low power neural LNA is presented in UMC 0.13 μm 1P8M CMOS technology. The amplifier can be biased adaptively from 200 nA to 2 μA , modulating input referred noise from 9.92 μV to 3.9μV . We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. Optimum sizing of the input transistors minimizes the accentuation of the input referred noise of the amplifier. It obviates the need of large input coupling capacitance in the amplifier which saves considerable amount of chip area. In vitro experiments were performed to validate the applicability of the neural LNA in neural recording systems. ADC is another important block in a NRFE. An 8-bit SAR ADC along with the input and reference buffer is implemented in 0.13 μm CMOS technology. The use of ping-pong input sampling is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the output data rate, the A/D process is only enabled through a proposed activity dependent A/D scheme which ensures that the background noise is not processed. Based on the dynamic range requirement, the ADC resolution is adjusted from 8 to 1 bit at 1 bit step to reduce power consumption linearly. The ADC consumes 8.8 μW from1Vsupply at1MS/s and achieves ENOB of 7.7 bit. The ADC achieves FoM of 42.3 fJ/conversion in 0.13 μm CMOS technology. Power consumption in SARADCs is greatly benefited by CMOS scaling due to its highly digital nature. However the power consumption in the capacitive DAC does not scale as well as the digital logic. In this thesis, two energy-efficient DAC switching techniques, Flip DAC and Quaternary capacitor switching, are proposed to reduce their energy consumption. Using these techniques, the energy consumption in the DAC can be reduced by 37 % and 42.5 % compared to the present state-of-the-art. A novel concept of code-independent energy consumption is introduced and emphasized. It mitigates energy consumption degradation with small input signal dynamic range.

The desire to enhance and make ourselves better is not a new one and it has continued to intrigue throughout the ages. Individuals have continued to seek ways to improve and enhance their well-being for example through nutrition, physical exercise, education and so on. Crucial to this improvement of their well-being is improving their ability to remember. Hence, people interested in improving their well-being, are often interested in memory as well. The rationale being that memory is crucial to our well-being. The desire to improve one’s memory then is almost certainly as old as the desire to improve one’s well-being. Traditionally, people have used different means in an attempt to enhance their memories: for example in learning through storytelling, studying, and apprenticeship. In remembering through practices like mnemonics, repetition, singing, and drumming. In retaining, storing and consolidating memories through nutrition and stimulants like coffee to help keep awake; and by external aids like notepads and computers. In forgetting through rituals and rites. Recent scientific advances in biotechnology, nanotechnology, molecular biology, neuroscience, and information technologies, present a wide variety of technologies to enhance many different aspects of human functioning. Thus, some commentators have identified human enhancement as central and one of the most fascinating subject in bioethics in the last two decades. Within, this period, most of the commentators have addressed the Ethical, Social, Legal and Policy (ESLP) issues in human enhancements as a whole as opposed to specific enhancements. However, this is problematic and recently various commentators have found this to be deficient and called for a contextualized case-by-case analysis to human enhancements for example genetic enhancement, moral enhancement, and in my case memory enhancement (ME). The rationale being that the reasons for accepting/rejecting a particular enhancement vary depending on the enhancement itself. Given this enormous variation, moral and legal generalizations about all enhancement processes and technologies are unwise and they should instead be evaluated individually. Taking this as a point of departure, this research will focus specifically on making a case for ME and in doing so assessing the ESLP implications arising from ME. My analysis will draw on the already existing literature for and against enhancement, especially in part two of this thesis; but it will be novel in providing a much more in-depth analysis of ME. From this perspective, I will contribute to the ME debate through two reviews that address the question how we enhance the memory, and through four original papers discussed in part three of this thesis, where I examine and evaluate critically specific ESLP issues that arise with the use of ME. In the conclusion, I will amalgamate all my contribution to the ME debate and suggest the future direction for the ME debate.