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1. WO2020021542 - MOYENS ET PROCÉDÉS POUR UN SYSTÈME ET UN TRAITEMENT PERSONNALISÉS D'ÉVALUATION DE LA SANTÉ COMPORTEMENTALE

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

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CLAIMS

3. A bio-feedbacking system characterized by

f. a user-derived, internal-data source, captive portal data input (CP); said data is selected from a group consisting of data comprising basic input data, passive continuous input data and active input data;

g. a external-data source database;

h. a graphical user interface (GUI);

i. an EEG wearable device configured for both sensing and for stimulating defined area of patient’s brain; and

j. a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial electrode mediated electro-stimulation to stimulate said area of patient’s brain according to said patient-data driven stimulating-protocol.

4. A bio-feedbacking system characterized by

f. a user-derived, internal-data source, captive portal data input (CP); said data is selected from a group consisting of data comprising basic input data, passive continuous input data and active input data;

g. an external-data source database;

h. a graphical user interface (GUI);

i. a HEG wearable device configured for both sensing and for stimulating defined area of patient’s brain; and

j. a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial HEG electrode to stimulate said area of patient’s brain according to said patient-data driven protocol.

5. A bio-feedbacking system characterized by

f. a user-derived, internal-data source, captive portal data input (CP); said data is selected from a group consisting of data comprising basic input data, passive continuous input data and active input data;

g. an external-data source database;

h. a graphical user interface (GUI);

i. an EEG or HEG wearable device configured for both sensing and stimulating defined area of patient’s brain; and

j. a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial EEG or HEG electrode to stimulate said area of patient’s brain according to said patient-data driven protocol.

6. A bio-feedbacking system useful for the treatment of at least one of eating disorders, obesity, uncontrolled or unbalanced eating, diabetes, sleep disorders, sleep apnea and uncontrolled behaviors, characterized by:

f. a user-derived, internal-data source, captive portal data input (CP); said data is selected from a group consisting of data comprising basic input data, passive continuous input data and active input data;

g. a external-data source database;

h. a graphical user interface (GUI);

i. an EEG wearable device configured for both sensing and for stimulating defined area of patient’s brain; and

j. a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial electrode mediated electro-stimulation to stimulate said area of patient’s brain according to said patient-data driven stimulating-protocol.

7. The system of claim 4, wherein said system is an adjunctive therapy for at least one of therapies for eating disorders, uncontrolled or unbalanced eating, diabetes, sleep disorders, sleep apnea and uncontrolled behaviors.

8. A multilayered bio-feedbacking system comprising:

e. a user derived module, comprising an internal-data source captive portal data input (CP); said data is selected from a group consisting of input data, passive continuous input data and active input data;

f. an external module comprising database derived from user’s logged behavior;

g. a wearable module, intercommunicable with said user derived module, comprising:

i. at least one first sensor and at least one second sensor;

said at least one first sensor is configured to log at least one first user’s behavior, said at least one first behavior is characterized by a series of n events, n is an integer number being greater than or equals 1 ; and,

said at least one second sensor is configured to log at least one second user’s behavior, said at least one second behavior is characterized by a series of m events, m is an integer number being greater than or equals 1; and, ii. at least one first and at least one second stimulation modules for stimulating a response for said at least one first and second user’s behaviors, respectively, said response is stimulated in connection with said n’ and m’ events; n’ and m’, respectively, are an integer numbers, each of which is being greater than or equals 1 ; each of which of said at least one first and second stimulation modules are in connection with either or both

(1) one or more signal inducers configured for a conscious alert; and

(2) one or more signal inducers configured for either a conscious or a subconscious stimulation at one or more locations of user’s brain; h. a multilayered supervising processor structured with at least one first stimulus-respond reflex lower-layer and at least one second processing-supervising reflex upper-layer; said processor comprises a user driven behaviors-hierarchy optimizer configuring for storing and processing parameters derived from user’s behavior, weighing and defining hierarchy of the same, and either offline or online providing critical go/no-go values and allowable measures’ range for said parameters; said processor is configured for operating as follows:

i. in said lower reflex layer, and for both at least one first and at least one second behaviors, by means of at least a portion of said sensors intercommunicated with said wearable module, detecting said n and m events and defining the same as stimulus;

ii. by communicating with said behaviors-hierarchy optimizer, correlating said stimulus with at least one parameter derived from user’s at least one first behavior, weighting said parameter, and subsequently defining a response for said at least one first behavioral stimulus;

iii. in said upper reflex-like layer, and for both at least one first and at least one second behaviors, weighting said n and in events; detecting prevalence and magnitude of said events, processing the same, and supervising said lower reflex-like layer such that one of the following is being held true:

(1) decreasing response for stimuli of said first behavior if both hierarchy of second behavior is higher than hierarchy of first behavior prevalence and magnitude of said first behavior is lower than prevalence and magnitude of said second behavior;

(2) decreasing response for stimuli of said first behavior if both hierarchy of second behavior is higher than hierarchy of first behavior prevalence, magnitude of said first behavior is higher than prevalence and magnitude of said second behavior; and at least one parameter derived from user’s first behavior is lower than critical go/no-go, and values of said at least one said pre-determined parameter are within allowable measures’ range ;

(3) allowing response for both stimuli of said of said first behavior and stimuli of said second behavior, if both hierarchy of second behavior is higher than hierarchy of first behavior, prevalence and magnitude of said first behavior is lower than prevalence and magnitude of said second behavior, and and at least one parameter derived from user’s first behavior is higher than critical go/no-go, and values of said at least one said pre-determined parameter are not within allowable measures’ range ;

(4) allowing response for both stimuli of said of said first behavior and stimuli of said second behavior, if both hierarchy of second behavior is higher than hierarchy of first behavior, prevalence and magnitude of said first behavior is higher than prevalence and magnitude of said second behavior, and at least one parameter derived from user’s first behavior is higher than critical go/no-go, and values of said at least one said pre-determined parameter are not within allowable measures’ range ;

9. The multilayered bio-feedbacking system of any of the above claims, wherein said parameters derived from user’s behavior comprise data driven from user’s behavior; user’s location at time, user’s adjacent mapping and user’s close and remote environment and coordinates thereof, user’s scheduled activity, user’s physical, physiological, biological, chemical and emotional quantifiably parameters, and hierarchy thereof; distance travelled measured by an accelerometer, velocity, heart rate, blood pressure, body temperature, sleeping time, duration of phone calls, numbers of outgoing and incoming calls and text messages, identification of calls and callers, number of calls, length of calls, number of unique calls, number and duration of visits in restaurants, and fast food sites, sport’s sites including swimming pools, gym, camera photos, location, acquisitions, electrical activity of the brain, mood parameters, including variability and frequency of mood change, increased blood pressure, prolonged skin problems, extreme change in appetite, excess gas, frequent dizziness, gastric ulcer, myocardial infarction, inability to work, nightmares, feeling incompetent in all areas, desire to escape everything, apathy, depression or prolonged anger, excessive tiredness, thinking/talking over and over about the same topic, irritability for no apparent reason, daily distress/anxiety, emotional hypersensitivity, loss of sense of humor, cold hands and/or feet, dry mouth, stomach pain, increased sweating, muscle tension, tightening of the jaw/teeth grinding, transient diarrhea, insomnia, tachycardia (increased heart rate) hyperventilation (increased respiratory rate), sudden or transient increased blood pressure, change in appetite, surge of motivation, sudden enthusiasm, sudden urge to start new projects, memory problems, general malaise without specific cause, tingling of the extremities, feeling of constant physical strain, change in appetite, skin problems, increased blood pressure, constant tiredness, gastritis/gastric ulcer, dizziness/feeling as if floating, excessive emotional sensitivity, self-doubt, constant thought about the same topic, constant irritability, decreased libido, frequent diarrhea, sexual difficulties, insomnia, nausea, tics and any combination thereof.

10. The system of any of the above claims, wherein said captive portal is configured to collect and store said subject’s data input.

11. The system of any of the above claims, wherein said system is either stationary or mobile.

12. The system of any of the above claims, wherein said CP comprises an algorithm configured to weigh the results of said data input, with said data of said database.

13. The system of any of the above claims, wherein said instructions for electrostimulation comprises at least one of the group consisting of a wearable neurofeedback (NF) system and a wearable neurofeedback system using virtual reality (VR)

14. The neurofeedback of any of the above claims, wherein said system comprises at least one EEG electrode configured to stimulate at least one brain area.

15. The system of any of the above claims, wherein said system mammalian subject is a human patient.

16. The system of any of the above claims, wherein said system is useful for treating eating disorders.

17. The system of any of the above claims, wherein said system is useful for increasing at least one selected from the group consisting of ability to organize, persistence, temptation resistance, devoting, awareness of the need to change., abilities to persist, planning , optimism, being essential, being organized , being aware of quality, being attentive to hunger, managing to be particular, motivating to be particular, readiness for treatment, sleeping quality , faith in own ability, and any combination thereof .

18. The system of any of the above claims, wherein said system is useful for treating at least one condition selected from emotional eating , anxiety , impulsiveness, frustration of food, urge to eat , physical problems, and any combination thereof .

19. The system of any of the above claims, wherein said system is useful for treating at least one of a group consisting of eating disorders, unbalanced eating, uncontrolled eating or obesity, ADHD, addictions, ADD, eating behaviors, depression, anxiety, autism, as well as eating accompanying diseases anxiety addictions, pain, sexuality and fertility , fibromyalgia performance , sleep disorders and any combination thereof.

20. The system of any of the above claims, said eating disorders are selected from a group consisting non-diagnosed eating disorders, unbalanced eating, uncontrolled eating, industrial eating, obesity, anorexia nervosa, bulimia nervosa, muscle dysmorphia, Binge Eating Disorder, Other Specified Feeding or Eating Disorder, atypical anorexia nervosa, atypical bulimia nervosa, Eating Disorders, disorders with symptoms similar to anorexia or bulimia that do not meet all diagnostic criteria for DSM disorders and any combination thereof .

21. The system of any of the above claims, wherein said human patient is selected from a group of patients not diagnosed with obesity, patient diagnosed with obesity, patient diagnosed with AD(H)D, patient not diagnosed with AD(H)D, patient diagnosed with eating disorders, and patient not diagnosed with eating disorders.

22. The CPM of any of the above claims, wherein said CPM further provides instructions for cannabinoid- based therapy adjunct to said cranial electrode mediated electro stimulation.

23. The system of any of the above claims, wherein eating disorders are selected from a group consisting of: Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder (BED), Avoidant/Restrictive Food Intake Disorder (ARFID), obesity, unbalanced eating, selective eating, western industrial eating, orthorexia, excessive exercise and any combination thereof.

24. The system of any of the above claims, wherein Passive Continuous Input Data comprises at least one of said subject’s parameters selected from EEG, distance travelled, velocity, heart rate, blood pressure, body temperature, sleeping time, duration of phone calls, number of outgoing and incoming calls and text messages, identification of phone calls and callers, length of calls, WhatsApp messages, Social networks’ usage, visits in restaurants, visits in in fast food sites, visits in swimming pools, visits in gym , camera photos, location, acquisitions and any combination thereof.

25. The system of any of the above claims, wherein Passive Continuous Input Data is assembled by a device is selected from a group of an EEG device, a camera, a mobile phone, a smartphone, a watch, a smart watch, a bracelet, a smart bracelet, a wristband, a smart wristband, a smart band and any combination thereof.

26. The system of any of the above claims, wherein Basic Input Data comprises personal details, said personal details are weight, Body fat, height, age, BMI, body fat, muscle mass and gender.

27. The system of any of the above claims, wherein Active Input Data comprises at least one of a personal characterization questionnaire, an eating and diet preferences questionnaire, a “Health promoting questionnaire " and any combination thereof.

28. The system of any of the above claims, configured to repeat to collect said data following said treatment of said eating disorders at plurality of time points, to determine whether the subject is responsive; and to recommend the stimulation to be continued if the subject is responsive or to be discontinued is the subject is non responsive.

29. The system of any of the above claims, wherein said CP further configured to collect said data following said treatment of said eating disorders, at n time points, wherein n is an integer equal of higher than 2, comprising of first time point before start of said treatment of said eating disorders and a second time point at a later time over life of said mammalian subject; further wherein said CMP configured to provide instructions for cranial electrode mediated electro stimulation to said areas in the brain according to a predetermined patient data dependent protocol, and said database provides data related to eating disorders, and said stimulation be continued, if values of said mammalian subject’s weight in said input data at second time point are lower than value at said first time point i.e. subject.

30. A method of bio-feedbacking, said method characterized by

f. providing a user-derived, internal-data source, captive portal data input (CP); said data is selected from a group consisting of data comprising basic input data, passive continuous input data and active input data;

g. providing a external-data source database;

h. providing a graphical user interface (GUI);

i. providing an EEG wearable device configured for both sensing and for stimulating defined area of patient’s brain; and

j. providing computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial electrode mediated electro-stimulation to stimulate said area of patient’s brain according to said patient-data driven stimulating-protocol.

31. A method of treating at least one of eating disorders, obesity, uncontrolled or unbalanced eating, diabetes, sleep disorders, sleep apnea and uncontrolled behaviors. The method characterized by:

f. providing a user-derived, internal-data source, captive portal data input (CP); said data is selected from a group consisting of data comprising basic input data, passive continuous input data and active input data;

g. providing a external-data source database;

h. providing a graphical user interface (GUI);

i. providing an EEG wearable device configured for both sensing and for stimulating defined area of patient’s brain; and

j . providing a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial electrode mediated electro-stimulation to stimulate said area of patient’s brain according to said patient-data driven stimulating-protocol.

32. A method of bio-feedbacking by means of a multilayered system, said method comprising: g. providing a user derived module, comprising an internal-data source captive portal data input (CP); said data is selected from a group consisting of input data, passive continuous input data and active input data;

h. providing an external module comprising database derived from user’s logged behavior;

i. providing a wearable module, intercommunicable with said user derived module, this family of steps comprising steps of

i. providing at least one first sensor and at least one second sensor;

said at least one first sensor is configured to log at least one first user’s behavior, said at least one first behavior is characterized by a series of n events, n is an integer number being greater than or equals 1 ; and,

said at least one second sensor is configured to log at least one second user’s behavior, said at least one second behavior is characterized by a series of m events, m is an integer number being greater than or equals 1; and,

ii. providing at least one first and at least one second stimulation modules for stimulating a response for said at least one first and second user’s behaviors, respectively, said response is stimulated in connection with said n’ and m’ events; n’ and m’, respectively, are an integer numbers, each of which is being greater than or equals 1 ; each of which of said at least one first and second stimulation modules are in connection with either or both

(1) one or more signal inducers configured for a conscious alert; and

(2) one or more signal inducers configured for either a conscious or a subconscious stimulation at one or more locations of user’s brain; j. providing a multilayered supervising processor structured with at least one first stimulus-respond reflex lower-layer and at least one second processing-supervising reflex upper-layer; said processor comprises a user driven behaviors -hierarchy

optimizer configuring for storing and processing parameters derived from user’s behavior, weighing and defining hierarchy of the same, and either offline or online providing critical go/no-go values and allowable measures’ range for said parameters; k. configuring said processor for operating as follows: in said lower reflex layer, and for both at least one first and at least one second behaviors, by means of at least a portion of said sensors intercommunicated with said wearable module, detecting said n and m events and defining the same as /ith or m* stimulus; by communicating with said behaviors-hierarchy optimizer, correlating said stimulus with at least one parameter derived from user’s at least one first behavior, weighting said parameter, and subsequently defining a response for said at least one first behavioral stimulus; in said upper reflex-like layer, and for both at least one first and at least one second behaviors, weighting said n and m events; detecting prevalence and magnitude of said events, processing the same, and

l. supervising said lower reflex-like layer such that one of the following is being held true:

(1) decreasing response for stimuli of said first behavior if both hierarchy of second behavior is higher than hierarchy of first behavior prevalence and magnitude of said first behavior is lower than prevalence and magnitude of said second behavior;

(2) decreasing response for stimuli of said first behavior if both hierarchy of second behavior is higher than hierarchy of first behavior prevalence, magnitude of said first behavior is higher than prevalence and magnitude of said second behavior; and at least one parameter derived from user’s first behavior is lower than critical go/no-go, and values of said at least one said pre-determined parameter are within allowable measures’ range ;

(3) allowing response for both stimuli of said of said first behavior and stimuli of said second behavior, if both hierarchy of second behavior is higher than hierarchy of first behavior, prevalence and magnitude of said first behavior is lower than prevalence and magnitude of said second behavior, and at least one parameter derived from user’s first behavior is higher than critical go/no-go, and values of said at least one said pre-determined parameter are not within allowable measures’ range ;

(4) allowing response for both stimuli of said of said first behavior and stimuli of said second behavior, if both hierarchy of second behavior is higher than hierarchy of first behavior, prevalence and magnitude of said first behavior is higher than prevalence and magnitude of said second behavior, and at least one parameter derived from user’s first behavior is higher than critical go/no-go, and values of said at least one said pre-determined parameter are not within allowable measures’ range.

33. A method of bio-feedbacking as defined in any of the above claims, wherein said parameters derived from user’s behavior comprise data driven from user’s behavior; user’s location at time, user’s adjacent mapping and user’s close and remote environment and coordinates thereof, user’s scheduled activity, user’s physical, physiological, biological, chemical and emotional quantifiably parameters, and hierarchy thereof; distance travelled measured by an accelerometer, velocity, heart rate, blood pressure, body temperature, sleeping time, duration of phone calls, numbers of outgoing and incoming calls and text messages, identification of calls and callers, number of calls, length of calls, number of unique calls, number and duration of visits in restaurants, and fast food sites, sport’s sites including swimming pools, gym, camera photos, location, acquisitions , electrical activity of the brain, mood parameters, including variability and frequency of mood change, increased blood pressure, prolonged skin problems, extreme change in appetite, excess gas, frequent dizziness, gastric ulcer, myocardial infarction, inability to work, nightmares, feeling incompetent in all areas, desire to escape everything, apathy, depression or prolonged anger, excessive tiredness, thinking/talking over and over about the same topic, irritability for no apparent reason, daily distress/anxiety, emotional hypersensitivity, loss of sense of humor, cold hands and/or feet, dry mouth, stomach pain, increased sweating, muscle tension, tightening of the jaw/teeth grinding, transient diarrhea, insomnia, tachycardia (increased heart rate) hyperventilation (increased respiratory rate), sudden or transient increased blood pressure, change in appetite, surge of motivation, sudden enthusiasm, sudden urge to start new projects, memory problems, general malaise without specific cause, tingling of the extremities, feeling of constant physical strain, change in appetite, skin problems, increased blood pressure,

constant tiredness, gastritis/gastric ulcer, dizziness/feeling as if floating, excessive emotional sensitivity, self-doubt, constant thought about the same topic, constant irritability, decreased libido, frequent diarrhea, sexual difficulties, insomnia, nausea, tics and any combination thereof.

34. A method of bio-feedbacking as defined in any of the above claims, wherein said wherein said captive portal is configured to collect and store said subject’s data input.

35. A method of bio-feedbacking as defined in any of the above claims, wherein said wherein said system is either stationary or mobile.

36. A method of bio-feedbacking as defined in any of the above claims, wherein said wherein said CP comprises an algorithm configured to weigh the results of said data input, with said data of said database.

37. A method of bio-feedbacking as defined in any of the above claims, wherein said wherein said instructions for electrostimulation comprises at least one of the group consisting of a wearable neurofeedback (NF) system and a wearable neurofeedback system using virtual reality (VR)

38. A method of bio-feedbacking as defined in any of the above claims, wherein said system comprises at least one EEG electrode configured to stimulate at least one brain area.

39. A method of bio-feedbacking as defined in any of the above claims, wherein said system mammalian subject is a human patient.

40. A method of bio-feedbacking as defined in any of the above claims, wherein said system is useful for treating eating disorders.

41. A method of bio-feedbacking as defined in any of the above claims, wherein said eating disorders are selected from a group consisting non-diagnosed eating disorders, unbalanced eating, uncontrolled eating, industrial eating, obesity, anorexia nervosa, bulimia nervosa, muscle dysmorphia, Binge Eating Disorder, Other Specified Feeding or Eating Disorder, atypical anorexia nervosa, atypical bulimia nervosa, Eating Disorders, disorders with symptoms similar to anorexia or bulimia that do not meet all diagnostic criteria for DSM disorders and any combination thereof .

42. A method of bio-feedbacking as defined in any of the above claims, wherein said human patient is selected from a group of patients not diagnosed with obesity, patient diagnosed

with obesity, patient diagnosed with AD(H)D, patient not diagnosed with AD(H)D, patient diagnosed with eating disorders, and patient not diagnosed with eating disorders.

43. A method of bio-feedbacking as defined in any of the above claims, wherein said CPM further provides instructions for cannabinoid- based therapy adjunct to said cranial electrode mediated electro stimulation.

44. A method of bio-feedbacking as defined in any of the above claims, wherein said eating disorders are selected from a group consisting of: Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder (BED), Avoidant/Restrictive Food Intake Disorder (ARFID), obesity, unbalanced eating, selective eating, western industrial eating, orthorexia, excessive exercise and any combination thereof.

45. A method of bio-feedbacking as defined in any of the above claims, wherein said Passive Continuous Input Data comprises at least one of said subject’s parameters selected from distance travelled, velocity, heart rate, blood pressure, body temperature, sleeping time, duration of phone calls, number of outgoing and incoming calls and text messages, identification of phone calls and callers, length of calls, WhatsApp messages, Social networks’ usage, visits in restaurants, visits in in fast food sites, visits in swimming pools, visits in gym, camera photos, location, acquisitions and any combination thereof.

46. A method of bio-feedbacking as defined in any of the above claims, wherein said Passive Continuous Input Data is assembled by a device is selected from a group of a mobile phone, a smartphone, a watch, a smart watch, a bracelet, a smart bracelet, a wristband, a smart wristband, a smart band and any combination thereof.

47. A method of bio-feedbacking as defined in any of the above claims, wherein said Basic Input Data comprises personal details, said personal details are weight, BMI, body fat , muscle mass, height, age and gender.

48. A method of bio-feedbacking as defined in any of the above claims, wherein said Active Input Data comprises at least one of a personal characterization questionnaire, an eating and diet preferences questionnaire, a“Health promoting questionnaire " and any combination thereof.

49. A method of bio-feedbacking as defined in any of the above claims, wherein said CP is configured to repeat to collect said data following said treatment of said eating disorders at plurality of time points, to determine whether the subject is responsive; and to recommend the stimulation to be continued if the subject is responsive or to be discontinued is the subject is non responsive.

50. A method of bio-feedbacking as defined in any of the above claims, wherein said CP further configured to collect said data following said treatment of said eating disorders, at n time points, wherein n is an integer equal of higher than 2, comprising of first time point before start of said treatment of said eating disorders and a second time point at a later time over life of said mammalian subject; further wherein said CMP configured to provide instructions for cranial electrode mediated electro stimulation to said areas in the brain according to a predetermined patient data dependent protocol, and said database provides data related to eating disorders, and said stimulation be continued, if values of said mammalian subject’s weight in said input data at second time point are lower than value at said first time point i.e. subject.

51. A personalized portable system configured for a rapid diagnosis of a mammalian subject , said system characterized by:

e. an EEG wearable device configured for both sensing and for stimulating defined area of patient’s brain;

f. a user-derived, internal-data source, captive portal data input (CP); said data comprises basic input data, algorithm-based questionnaires ; and measurements of said EEG;

g. a graphical user interface (GUI); and

h. a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial electrode mediated electro-stimulation to stimulate said area of patient’s brain according to said patient-data driven stimulating-protocol.

52. A method for rapid diagnoses of a mammalian subject , said method comprising:

f. providing a user derived module, comprising an internal-data source captive portal data input (CP); said data comprises basic input data, algorithm-based questionnaires ; and measurements of said EEG;

g. providing an EEG wearable device configured for both sensing and for stimulating defined area of patient’s brain;

h. providing a external-data source database;

i. providing a graphical user interface (GUI); and

j . providing a computer processing manager (CPM) for processing both said internal data and external data, interconnected with said CP, said database and said GUI; said CPM is configured to instruct cranial electrode mediated electro-stimulation to stimulate said area of patient’s brain according to said patient-data driven stimulating-protocol.