A method for estimating a brain activity response following a stimulus of a person comprises the steps: providing a usage data set of the person from a personal device used by said person, wherein at least one usage attribute is associated to said usage data set, wherein attribute data is associated to each of the at least one usage attribute, providing a computational inference model, generated from a plurality of brain activity data sets and a plurality of usage data sets, wherein each brain activity data set comprises data derived from a brain activity response following a sensory stimulus, submitting the attribute data of each of the at least one usage attributes to said computational inference model, estimating a brain activity response following a sensory stimulus of said person by evaluating said computational inference model for the submitted attribute data. The method is useful to determine, for example the influence of intensive touch pad usage (of a smartphone) on somatosensory evoked potentials.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. Method for estimating a brain activity response (1) following a stimulus (S) of a person comprising the steps: providing a usage data set (2) of the person from a personal device (10) used by said person, wherein at least one usage attribute (3) is associated to said usage data set (2), wherein attribute data (4) is associated to each of the at least one usage attribute (3), providing a computational inference model (5) generated from a plurality of brain activity data sets (6) and a plurality of usage data sets (2), wherein each brain activity data set (6) comprises data derived from a brain activity response (1) following a sensory stimulus (S), submitting (109) the attribute data (4) of each of the at least one usage attributes (3) to said computational inference model (5), estimating (110) a brain activity response (1) following a sensory stimulus (S) of said person by evaluating said computational inference model (5) for the submitted attribute data (4). 2. Method according to claim 1, characterized in that the computational inference model (5) is generated by conducting the following steps: providing a plurality of brain activity data sets (6), providing a plurality of said usage data sets (2), acquired from said person or from a plurality of persons, estimating for each usage data set (2) of the plurality of usage data sets (2) the attribute data (4) associated to the at least one usage attribute (3), yielding for each usage attribute (3) a plurality of attribute data (4), submitting (106, 107) the plurality of brain activity data sets (6) and the plurality of the attribute data (4) to a regression analysis, particularly a multiple linear regression analysis or a machine learning algorithm, wherein said regression analysis determines (108) said computational inference model (5). 3. Method according to claim 2, characterized in that the regression analysis is a multiple linear regression analysis wherein said regression analysis is designed such that an event-related coefficient (7) for each of the at least one usage attribute (3) is determined, wherein the computational inference model (5) is particularly a function that relates the attribute data (4) associated to the usage attribute (3) by means of the corresponding event-related coefficient (7) to a brain activity response (1) following a sensory stimulus (S). 4. Method according to claim 1, characterized in that the at least one usage attribute (3) is/are: a use per hour of the personal device (10) by the person, particularly within the past ten days, an age at which the person first began using the personal device (10), a time elapsed from a period of intense use of the personal device (10) by the person to a measurement of brain activity for providing brain activity data a number of touchscreen events of the personal device (10) per unit of time, a distribution of time intervals between touchscreen events of the personal device (10), and/or a parameter, particularly a power law exponent, of a distribution of time intervals between touchscreen events of the personal device (10). 5. Method according to claim 1, wherein the computational inference model (5) is a personal inference model (51), wherein said plurality of brain activity data sets (6) is acquired repeatedly from a person and wherein the plurality of the usage data sets (2) is acquired from the same person. 6. Method according to claim 1, wherein the computational inference model (5) is a computational population inference model (52), wherein said plurality of brain activity data sets (6) is acquired particularly repeatedly, from a plurality of persons and wherein the plurality of the usage data sets (2) is acquired from the same plurality of persons. 7. Method according to claim 1, wherein the usage data set (2) is provided to the computational inference model (5) comprising a temporal resolution of at least ten minutes, and wherein said usage data set (2) is particularly acquired from a battery log, a touch screen log, a keypad log, a data exchange log and/or an accelerometer of the personal device (10). 8. Method according to claim 1, wherein the usage data set (2) is provided by a personal device (10) comprising a body-machine interface, particularly a touch screen and/or a keypad, and wherein said personal device (10) is particularly a smart phone, a tablet computer, or any additional sensor linked, e.g. by means of bluetooth or e.g. a cable, to the smart phone or tablet computer, such as e.g. a smartwatch, an exercise band, a headset, a head-mounted display, or a heart rate monitor. 9. Method according to claim 1, wherein the personal device (10) is used by the person on average at least five times per week and at least 15 minutes per day. 10. Method according to claim 1, wherein the computational inference model (5) is a spatiotemporal scalp map (SM), particularly comprising the same temporal resolution as the brain activity dataset (6). 11. Method according to claim 1, wherein each of the plurality of brain activity data sets (6) is acquired by an electroencephalography method, a functional magnet resonance imaging method, a positron emission tomography method, a functional near-infrared spectroscopy and/or an electrocorticography method. 12. Method according to claim 1, wherein an indicator, particularly a value is generated (112), wherein said indicator is a function of the estimated brain activity response (1) of said person and wherein said indicator furthermore is related particularly to a previously estimated indicator for the estimated brain activity response (1) of said person or to an average indicator for the estimated brain activity response (1) of said person or a plurality of persons. 13. Method according to claim 1, wherein the estimated brain activity response (1) to a sensory, particularly somatosensory, particularly a tactile stimulus (S) of said person by said evaluation of said computational inference model (5) is compared (111) to the estimated brain activity response (1) based on a computational inference model (5) that has been updated with at least one brain activity data set (6) that is recorded after said first estimation. 14. Method according to claim 1, wherein the usage data set (2) is generated (100) by an interaction of the person with the personal device (10), the usage data set (2) is particularly stored (101) on said personal device (10), the attribute data (4) is extracted (102) from said personal device (10), the attribute data (4) is particularly stored (103) in a usage database (20), the brain activity data set is acquired (104), and the brain activity data set (6) is stored (105) in a brain activity database (60). 15. Computer program comprising program code, wherein said program code prompts a computer to execute the method according to claim 1, if the computer program is loaded, installed or executed on the computer.