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NeuroTech
Table of Contents
1 Executive Summary
2 Business and service Description
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2.1 Business Description
2.2 Products and Services
2.3 intellectual property
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3 The Market
3.1. Industry Overview and Trends
3.2 Technological Trends
3.3 Market Trends
3.4 Target Market
3.5 Market Size
3.6 The Competition
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4 Sales and Marketing
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4.1 Customers
4.2 Suppliers
4.3 Product Inventory
4.4 Research and Development
4.5 Advertising and Promotion
4.6 Pricing and Distribution
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5 Operating Plan
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5.1 Business Location
5.2 Equipment
5.3 Technology requirements
5.4 Regulatory Compliance
5.5 Financial Management
5.6 Management Team
5.7 External Advisory Team
5.8 Key Employees
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6 Action Plan
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6.1 Investment 30
6.2 Key Milestones
6.3 Risk Assessment and Contingency Plan
6.4 SWOT Analysis
7 Financial Forecasts
7.1 Assumptions
7.2 Sales Forecast
7.3 Profit and Loss
7.3.1 Profit and Loss Table
7.3.2 Profit and Loss Chart
7.4 Balance Sheet
8 Conclusion
123-456-7890
March 14th, 1984
1.Executive Summary
Neuro Tech Inc. provides Cutting-edge technology to enable patients suffering from addiction or Substance Disorders to receive non-invasive drug-free treatment. The Company is comprised of a team of passionate and motivated engineers, and professionals, dedicated to developing pioneering neuromodulation treatments for various addiction or substance use disorders. In fact, the advancement of various signal processing methodologies like electroencephalogram (EEG), combined with Artificial Intelligence algorithms, enables Neuro Tech Inc. to develop an application using brain-based data for drug-free treatments of addiction. The pandemic has exacerbated the crisis of problematic substance use in Canada and federal government 2021 budget proposes to provide an additional $116 million for the substance use and addictions program to support a range of innovative approaches to harm reduction, treatment, and prevention at the community level.
Neuro Tech was incorporated as 1000370316ONTARIO INC. In November 2022, in Ontario; the Company rents an office in Oshawa. Ms. Leila Ahmadi owns 25% of the company and serves as a Chief Executive Officer; Mr. Shahin Bazeghi Kisomi owns 25% of the company and serves as a Chief Technology Officer. Ms. Bahareh Nazari owns 25% of the company and serves as a Chief Operating Officer. Ms. Anahita Kimyaghalam owns 25% of the company and serves as a Director of Business Management.
The Company has successfully built its minimum viable product (MVP) and tested it with ten patients. The results of this experience further confirmed the success of the product as it managed to obtain an 87.2% success rate.
Ms. Leila Ahmadi, Ms. Bahareh Nazari and Anahita Kimyaghalam are seeking a work permit to set up the business by conducting market research, optimizing product and prototype, and marketing activities as well as Searching and negotiating with reliable supplier(s). The Company will focus on conducting market research, product optimization, and marketing strategy implementation and target mainly Ontario province to start selling in year 1. In Year 2, Neurotech Inc. will mainly focus on developing relationships with Health and Substance Abuse and treatment Centers to sell its product and expand its market to Quebec. In year 3, the
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company will equip its own research lab to improve its product and sell its product across Canada. The Company also has a plan to expand the applications of Neurotech technology to help people facing a variety of neuropsychiatric health challenges such as Anxiety, Type 2 Diabetes, Insomnia and Obesity as well as expand the target area to include international nearby markets. Neuro Tech Inc.'s revenue is anticipated to start at $234,000 in Year 1 and grow to $1,836,000 in Year
2 Business and service Description
2.1 Business Description
Drug addiction remains one of the most prominent social problems in Canadian society. According to the results of the Canadian Drug Use and Monitoring Survey (CADUMS), the number of Canadians who are abusing illicit drugs has been rising during the pandemic. The use of substances is associated with negative social, public safety and economic consequences for people’s suffering from addiction. In Canada, approximately 21% of the population (about 6 million people) will experience a substance use disorder or addiction at some point in their lifetime. Drug addiction is a chronic disease characterized by compulsive, or uncontrollable, drug seeking and use despite harmful consequences and changes in the brain, which can be long lasting. These changes in the brain can lead to the harmful behaviors seen in people who use drugs. As addiction is a chronic disease, patients cannot simply stop using drugs for a few days and be cured. Most patients need long-term or repeated care to stop using completely and recover their lives. Addiction treatment must help the patient stop using drugs (Detox), stay drugfree (Rehab and Sober living) and be productive in the family, at work, and in society. The treatment for addiction remains stagnant and needs to be improved. Drug addiction is also a relapsing disease. According to the National Institute on Drug Abuse, two-thirds of individuals return to drug use within weeks of beginning release from the Substance Abuse Center and almost 85% of patients relapse within a year of treatment. Medications such as methadone or Suboxone can be a beneficial treatment for addiction and may decrease withdrawal symptoms and cravings and may decrease the risk of relapse and provide more comfortable detox and rehab processes. However, they are only one component of addiction treatment and carry some risks including side effects, such as difficulty concentrating, nausea, vomiting, and insomnia; physical dependence; and overdose. For instance, methadone is one drug that can be fatal at high doses, and a physician should always carefully monitor its use. Neuro Tech Inc. provides Neuromodulation treatments consisting of Collection, Diagnose, Treatment and Effectiveness
for various addiction or substance use disorders. The advancement of various signal processing methodologies like electroencephalogram (EEG), combined with Artificial Intelligence algorithms (Neuro Tech Software) to develop an application using brain-based data to develop brain maps and diagnoses of patients. Based on diagnoses results the treatment sessions consist of applying electrical current to the region of interest (ROI) using Transcranial direct current stimulation (tDCS) device. Such treatment is used to reduce the time and cost of patients' treatment. The company's product by influencing brain performance metrics would increase patients’ selfagency by restoring cognitive control performance that would produce increased sobriety rates. The Company has successfully built its minimum viable product (MVP) and tested it with ten patients. The results of this experience further confirmed the success of the product as it managed to obtain an 87.2% success rate. according to the market report 2022, the braincomputer interface market was valued at USD 1,435.15 million in 2021 and is expected to reach USD 3,136.87 million by 2027, registering a CAGR of 14.58% during the forecast period, 2022- 2027. The Brain-computer Interface Market is Segmented by Type (Invasive Brain-computer Interface, Non-invasive Brain-computer Interface, and Other Types), Application (Restoration of Disabilities, Repair of Brain Function, and Other Applications)
The non-invasive brain-computer interface segment is expected to witness significant growth. The non-invasive brain-computer interface segment is found to dominate the overall market owing to the high applicability of the technology and increasing neurological disorders. The development of non-invasive brain-computer interface devices based on EEG is expected to increase the mainstream accessibility of BCI technology. Moreover, growth in the number of approvals is also expected to supplement the market's growth. For instance, in April 2021, the United States Food and Drug Administration (FDA) approved the use of a brain-computer interface device to aid stroke patients with hand, wrist, and arm disabilities in their recovery. In fact, the rising usage of brain-computer interface (BCI) technology for the treatment of many patients suffering from mental and physical impairments can be used as a promising tool in rehabilitation medicine during COVID-19, is likely to have a positive impact on the market growth. Further, the brain-computer interface market is likely to show rapid growth due to the rising R&D
activities by the companies to improve the brain-computer interface technology and various technological advancements such as miniaturization of devices.
Among all the factors, the increasing prevalence of neurodegenerative disorders is the foremost factor expected to drive the brain-computer interface market, new data from Canadian Perspectives Survey Series such as that due to COVID-19 pandemic and the disruption and stress caused by it the number of Canadians consumption of cannabis, alcohol and tobacco products increased by 34%. Approximately 21% of the Canadian population (about 6 million people) will experience a substance use disorder or addiction at some point in their lifetime. According to Statista (2022), drug expenditures in Ontario costs Canadians $16 bn in 2021.
Neuro Tech Inc. will target the Brain computer Interface (BCI) market for treatment of patients suffering from psychological and addiction problems. Applying the B2B business model, the Company works closely with organizations such as Health and Substance Abuse Center, Residential Mental Health and Substance Abuse Facilities and Addiction Treatment Center in Ontario to assist them with providing non-invasive drug-free treatment services for their patients suffering from addiction. The company will target Ontario province as it has the greatest number of the clients, around 1,826 businesses, which can use Nero Tech Inc.'s product and service. The company will expand its market to Quebec and throughout Canada in the near future. The main competitors include those companies that are using BCI-based innovation such as Zentrela, NeuroQore, Nurosene, and g.tec medical. The Company plans to continue testing and improving its product by conducting 15 more tests with patients.
2.2 Products and Services
Neuro Tech Inc. uses non-invasive drug-free treatments for patients suffering from addiction by utilizing advanced signal processing methodologies like electroencephalogram (EEG) to identify specific regions of the brain relating to addiction/substance abuse, combined with Artificial Intelligence algorithms to develop an application using brain-based data to develop brain maps and diagnoses of patients. Based on diagnoses results the treatment sessions consist of applying electrical current to the region of interest (ROI) using Transcranial direct current stimulation
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(tDCS) device. The company approach includes four stages of Collection, Diagnosis, Treatment and Effectiveness.
Collection: Applying international 10-20 system to install non-invasive 21-channel EEG device on the head of patients, the brain-based data will be collected between 5-10 minutes and the data will be sent to the Neuro Tech application for diagnosis. It is important to notice that the patients should be in good health with no lateral disease and no mental problems.
Diagnosis: In this stage the patients’ brain data will be analyzed to better understand the cortical electrical activity in the brain. The Neuro Tech AI application will consist of a large enough database to categorize and compare the data with functional brain scans related to addiction and substance use disorders. Upon developing the brain map and relevant analysis the result can show if activities in the brain are too high or too low and to how brain cells are communicating with each other and create a personalized treatment plan for each patient. The application will be able to indicate the type and level of addiction from Low, Med and High level and recommend the number of required sessions as well as identify the most effective position of electrical sensors (anode and cathode) for the brain treatment.
Treatment: Neurobiological models of addiction seek to broaden the understanding of addiction as a brain disease. These models integrate classic psychological models with neurobiological responses. According to the Neurobiological model, individuals learn social rules, which are handled by a reflective system in the brain to control impulsive responses. Individuals in recovery exhibit persistent neurophysiological deficits affecting cognitive performance. The anterior cingulate cortex (ACC) contributes to two essential aspects of executive control: inhibitory control and performance monitoring. Performance monitoring processes include error detection and conflict monitoring, whereas inhibitory control restrains desired behaviors. Neuroscience models of cognitive control emphasize that when the ACC detects erroneous or conflicting behavior, a signal is sent to the Dorsolateral Prefrontal Cortex (DLPFC). The DLPFC modulates and
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sustains goal-oriented behaviors by influencing top-down cognitive control, directing behaviors away from incorrect, conflict-causing responses and toward correct, conflict-reducing responses. With regard to addiction and sobriety, these monitoring and modulating processes are valuable for detecting hazardous situations or behaviors that increase the likelihood of relapse.
Importantly, previous studies such as Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications (2012) have shown that reduced metabolic activity in these brain regions predicts relapse behaviors in patients. The company uses Transcranial Direct Current Stimulation (tDCS) to apply the electronic current to the recommended brain node on the DLFPC region of interest (F3 and F4). The use of electrical current stimulation is a trusted treatment as mentioned in studies such as Preclinical Evidence for the Mechanisms of Transcranial Direct Current Stimulation in the Treatment of Psychiatric Disorders (2021) for patients suffering from psychiatric disorders such as PTSD, depression, Parkinson's disease, and stroke recovery. The patients will treat maximum for 10 sessions three times per week and each session is 15 minutes using 2 mA ranges. Based on the application diagnosis, Anode will be connected to the F4 region to increase brain activities of the right semaphore of the brain and Cathode will be connected to the F3 region to decrease brain activities of the right semaphore of the brain. Effectiveness: In order to examine the effectiveness and the progress of the treatment, brain maps of the patient are provided at the first, middle and the last session plus follow up sessions in future if needed. This can help to show the patient brain wave improvement patterns and track the progress with treatment as well as support the rehab sessions.
The Company has successfully built its minimum viable product (MVP) and tested it with ten patients.
2.3 intellectual property
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The company relies on its Neuro Tech Software to apply its services. The company does not have any patents or copyrights as of now. The Co-founders plan to apply through Canadian Intellectual Property Office (CIPO) after registering their Company and getting workpermit visa and file for patents and register trademarks to protect the company intellectual property such as Neuro Tech Software. In preparation for releasing the product to the public, the company will utilize a licensing management system to properly issue its technology to clients.
3 The Market
3.1. Industry Overview and Trends
The impact of COVID-19 on the market will be significant in the short run as the global supply chain is disrupted and manufacturing facilities are under complete shutdown; however, the rising usage of brain-computer interface (BCI) technology for the treatment of many patients suffering from physical impairments will enhance patients’ quality of life. BCI can be used as an assistive technology to monitor brain activity and translate specific signal features that reflect the Patient’s intent into commands and enable them to improve their motor and cognitive abilities, which can be a promising tool for rehabilitation medicine during COVID-19. According to the Market Research 2022, the brain-computer interface market is likely to show rapid growth due to the increasing prevalence of neurodegenerative disorders, rising R&D activities by the government to improve the brain-computer interface technology, and various technological advancements such as miniaturization of device. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period.
Many countries around the world have massive ongoing funding programs for brain-based research, and these initiatives include funding streams for AI and brain-inspired computing. The European Union’s Human Brain Project, for example, will provide an estimated EUR 1.19 billion over its mandate, while the United States’ BRAIN Initiative will invest an estimated USD 6.6 billion through the year 2027 and includes funding for public-private partnerships. Some of the major innovating markets in Asia are also investing heavily in this area of R&D. The China Brain Project is estimated to be worth USD 1 billion through 2030, while Japan is investing an estimated JPY 40 billion (more than CAD 435 million) in their Brain/Minds project.
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Canada has its own national brain research funding initiative, called the Canada Brain Research Fund, which has provided CAD 267 million in new funding as of 2021. Canada also invests heavily in the field of AI through, for example, CIFAR. However, there are also funding opportunities for BCI biotechnology startups, including the Ontario Brain Institute’s Neurotech Early Research and Development program and NERVE Program, which provides CAD 100,000 to up to 5 early-stage businesses annually, with the goal of developing a globally competitive neurotech cluster in Ontario. The Creative Destruction Lab also announced its newest funding stream in 2021, called The Neuro Stream, based in Toronto. Some of the areas of innovation under this stream specifically include BCI technology and neuroprosthetics.
There are several BCI-based innovation competitor companies who focus on healthcare application of BCI. In Ontario companies like Zentrela, NeuroQore and Nureosense are developing non-invasive technologies, including Zentrela’s electroencephalogram (EEG)-based neurotechnology platform to quantify the psychoactive effects of cannabis products, and NeuroQore’s repetitive transcranial magnetic stimulation (rTMS)-based platform to treat depression. Nurosene develops AI technologies to assist research into treatments for neurodegenerative diseases. The regulation of biotechnology in the United States and Canada is based on principles that reflect respect for the new technology, yet also recognize that most traditional approaches to regulation, employing sound science and common sense, still apply.
In Canada, Health Canada, the Canadian Food Inspection Agency, Fisheries and Oceans Canada and Environment Canada, have joint responsibility to regulate biotechnology-derived products. The definition used is found in the Canadian Environmental Protection Act (1999): "the application of science and engineering to the direct or indirect use of living organisms of parts or products of living organisms, in their natural or modified forms. The company uses the hardware for its product which already got the Food and Drug Administration (FDA) approval and has European Conformity (CE), and ISO13485 for the safety and quality of medical devices and will apply to get permission from Health Canada.
3.2 Technological Trends
Recent advances in computer hardware and signal processing have made possible the use of electroencephalogram (EEG) for communication between human brain and computers and this technology is known as brain-computer interface (BCI). Researchers have come out with a more convenient and safe method to obtain EEG signals by inventing a non-invasive technique to place the EEG sensors on the scalp of a human's head. The automatic classification of these signals is an important step towards making the use of EEG more practical in application and less reliant on trained professionals. The typical EEG classification pipeline includes artifact removal, feature extraction, and classification. On the most basic level, an EEG dataset consists of a 2D (time and channel) matrix of real values that represent brain-generated potentials recorded on the scalp associated with specific task conditions. This highly structured form makes EEG data suitable for machine learning.
A great number of traditional machine learning and pattern recognition algorithms have been applied on the EEG data. The availability of large datasets and the recent development of graphic processing units (GPU's) allow investigating deep learning architectures (neural network architectures containing at least two hidden layers). These innovations have led to an exponential increase in interest and applications of deep learning recently.
This advantage led to early adaptations in the realm of medical imaging which usually involves large datasets that are otherwise difficult to be interpreted, even by experts. Recently, due to the increasing availability of large EEG datasets, deep learning frameworks have been applied to the decoding and classification of EEG signals, which usually are associated with low signal to noise ratios (SNRs) and high dimensionality of the data. The company uses python with a classic supervised learning method to classify and analyze EEG signals in two different datasets at this stage. For each dataset specific goals are given in the respective description. Each dataset consists of single trials of spontaneous EEG activity, one part labeled (training data) and another part unlabeled (test data), and a performance measure. The goal is to infer labels for the test set from training data that maximize the performance measure for the true (but to the participant unknown) test labels. The company will dedicate a specific number of resources to maintenance
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and upgrade the software accordingly. As the clients are connected to the company server through the DaaS model, any update to the software will be implemented automatically. The company will apply an unsupervised learning method and deep learning method for its largest dataset and will follow and apply the latest trend in its hardware part.